A Review of Protocol Implementations and Energy Efficient Cross-Layer Design for Wireless Body Area Networks

2.1. Introduction

According to the World Health Organization (WHO) 17.3 million people died in 2008 from cardiovascular disease (CVD), heart attacks and strokes [3]. 80% of deaths took place in low and middle income countries. By 2030, WHO estimates that almost 23.6 million people would die from CVD. If the major risk factors for chronic diseases were eliminated, around three quarters of heart disease, stroke and type 2 diabetes would be prevented. Diabetes is a major risk factor for CVD [4]. Both type 1 and type 2 diabetes are closely linked to CVD, which is the major cause of deaths in people with diabetes, accounting for 50% of all diabetes fatalities. These statistics help define the context in which WBAN technology could make a major contribution to the management of life threatening diseases and increase patients' quality of life. Constant monitoring via WBAN technology has the potential to highlight key indicators in the progression of CVD and ensure adequate early warnings based on sensed data that is transmitted to health practitioners. Coupled with location awareness, fall detection technology, and patient triggered alarm notification, WBAN technology has the potential to offer a complete remote health management solution.

2.2. WBAN Positioning

In the spectrum of short range RF technologies, WBANs share the space with a number of short range protocols. IEEE 802.15 Task Group 6 describes a WBAN as: “low power devices operating in or around the human body (but not limited to humans) to serve a variety of applications including medical, consumer electronics /personal entertainment and other” [5]. A WBAN operates close to 1–2 m range or within the human body and is concerned with the connectivity of nodes operating within this space. A Wireless Personal Area Network (WPAN) is termed as a network operating up to 10 m around the human body and is normally associated with non-medical based communications such as MP3 or sports/fitness based applications. A Wireless Local Area Network (WLAN) typically has a range of up to 30 m depending on protocol type and has fixed power requirements.

A WBAN can be described as a specific type of Wireless Sensor Network (WSN) [6]. Figure 1 shows the context of a WBAN in relation to WPAN, WLAN and Wide Area Network (WAN). WBANs are normally associated with the human body, but not exclusively humans, where comparatively fewer nodes are deployed than traditional WSNs. With the small set of nodes, even limited data loss in WBANs could be significant, whereas data loss in a WSN is less of an issue as nodes may yield redundant information and can potentially source data from adjacent nodes. Therefore, each WBAN node needs to provide certain QoS guaranties. This may require additional measurements to ensure successful real-time data delivery, since reliability of measurements is vital in the medical domain. Both WSNs and WBANs are concerned with small footprint, low power sensing and have low data rate requirements. However a WSN is not designed for the specific set of challenges that are inherent in communications around and within the human body. This has led to a specific research focus on the medical application side of sensor networks and in particular the suitable communication protocols that are specific to WBANs.

2.3. Patient Monitoring & Condition Management

A WBAN can consist of a number of heterogeneous sensors and devices each having a separate role to play in the overall health management of the patient. The range of sensors that can be utilized as part of a WBAN includes ECG, blood oxidization, Electromyography (EMG), heart rate, BP, movement, and temperature [7,8].

Sensors can be combined with actuators to form a closed loop system where sensor data is utilized to manage the levels of medicine administered to the patient. A good example of this is the potential of Continuous Blood Glucose Monitoring (CBGM) and injection in type 1 diabetes patients [9–11]. An actuator wirelessly connected to the glucose sensor via a WBAN could administer correct doses of insulin based on the sensed glucose levels of the patient at any time. This system has obvious advantages over current manual methods of self test and injection. Figure 2 shows a typical WBAN together with the range of sensors that can be linked directly to a BCU.

In addition to wearable sensors, a WBAN can include implanted sensors such as cochlear implants and various types of nanosensors that can monitor key pre- and post-operative conditions. A WBAN comprising of implantable and wearable sensors can be combined with location management, alarm notification and fall detection for a complete system that could provide the necessary levels of remote monitoring for aged and vulnerable patients who require their independence.

2.4. WBAN System Architectures

The role of a fully functioning WBAN is to manage the communication of a set of heterogeneous nodes placed on or inside the human body. Implanted nodes may operate at 400 MHz in the case of Medical Implantable Communication Service (MICS), whilst wearable nodes operate within the Industrial Scientific Medical (ISM) band at 2.4 GHz. Due to the low power and short range limitations of sensor devices, a “sink” or BCU in the form of a mobile phone or bespoke portable device would receive the sensor output communications from the various nodes and perform any necessary local processing. The BCU would then act as a local gateway for further communications over, e.g., Wi-Fi or General Packet Radio Service (GPRS) to a remote server for further processing and monitoring. It is assumed that a BCU would have greater energy resource than a sensor node.

Figure 3 shows a typical BAN architecture where the personal device (mobile phone in this example) acts as the BCU to manage the data communications to the secure medical server via Wi-Fi, GPRS and the Internet. System designers must also cater for health practitioners initiating requests for key data from a specific sensor or a sensor group in priority to the normal steady state life-signs. This emergency data request could be in response to a specific health condition where a second sensor data reading is required as a priority to gain an overall picture of a potential condition. These alarm notifications would be triggered when a specific change in physiological readings is detected and highlights the closely coupled nature of individual sensors within a WBAN.

As WBAN technology becomes an integral component of healthcare management, the demands on the architecture are likely to be greater as the number and types of sensors increase along with data volumes. This means that architectures will need to cater for ever more demanding levels of energy efficiency and QoS requirements.

3.1. Introduction

WBANs are concerned with the remote monitoring of key physiological signals, therefore, systems need to maintain high levels of reliability and low levels of latency. Sensor devices have relatively low data rates but energy efficiency and node battery longevity are fundamental to enable devices to maintain connectivity for long periods of time. Patients are likely to be mobile and WBANs will be processing key sensitive data that must be securely processed. Nodes are likely to be worn continuously. Patients must have confidence in the technology and be comfortable with the device on a daily basis.

3.2. User Experience

Nodes are required to be placed on the body at key positions to enable effective monitoring of the relevant physiological conditions. A one size fits all, easy to wear wrist-band, will not at present deliver all the functionality required to monitor key vital life signs. Therefore, patients are likely to be required to place sensor nodes on key parts of the body themselves or allow them to be fitted by health practitioners in the hospital or patients' own home or workplace. Patients' interaction with the sensors, BCU and actuators must be included as a fundamental component of the design lifecycle of WBANs. As such the WBAN must be designed to be setup by non-technical users and from a patient perspective—easy to maintain and live with. A WBAN design that makes it obvious what operations are necessary at any point in time without reference to an instruction manual will achieve greater user acceptance [12].

If WBANs are to form an integral part of the remote healthcare management of patients, they need to be robust and resilient to cope with patients' lifestyles. Patients are likely to be mobile, either within the home or work environment. This may mean basic walking, sitting and lying down, or more vigorous activities such as running and climbing stairs. WBANs operating environments are likely to vary depending on the activity and postural position of the patient. Therefore, numerous activities are likely to result in Non-Line-of-Sight (NLOS) communications between transmitter and receiver. Spectrum interference is also likely due to the proximity of other devices in the ISM band. WBANs need to be resilient enough within the standard modes of operation to ensure patients and healthcare practitioners are not faced with intermittent and unreliable monitoring results. Patients and professionals must be able to rely and trust in WBAN technology. Wearability or reliability issues that are not fixed as part of formal usability and clinical trials in a rush to market, are likely to result in patient reticence, frustration and loss of confidence in the system. The patient demographic for WBANs is likely to be biased toward the more aged sectors of the population. Fear of technology, inexperience of smart devices, mobility and dexterity issues could all be factors that may be a barrier to patient take-up. As WBAN technology moves from the research lab to the market place, designers that incorporate user experience issues are more likely to succeed.

3.3. Energy Efficiency

Energy efficiency within a WBAN environment is a key requirement where batteries are likely to be operational for months or years rather than days. This is especially important in the case of embedded sensors where patients may have to undergo a minor operation to change the battery. A pacemaker or glucose monitor could require a battery life of five years or more [11]. Characteristics such as low duty cycle, high levels of node sleep duration, use of efficient protocols and link adaptation/power management techniques can all contribute to extended battery life.

A node may spend large amounts of time in sleep mode but can wake up frequently to communicate key physiological data. The energy consumed during the node wakeup process can be significant and degrade battery life. Propagation and signal absorption issues can be a significant problem for WBANs requiring specific energy efficient solutions. The optimal transmission power for on-body transmission for a wireless link depends on the physical distance of the link and its instantaneous channel condition [13]. Patient mobility and posture can have a significant effect on efficient packet delivery. The inherent characteristics of RF attenuation in and around the human body can have a direct impact on channel conditions [14].

There is a potential scope for some forms of energy harvesting especially with a mobile patient, but is only likely to deliver small amounts of energy [15]. However, mechanisms that use body heat or vibration seem well suited to energy harvesting and could form part of an overall energy efficient solution [11].

3.4. WBAN Traffic

Sources of data traffic in a WBAN are mostly medical sensors and actuators that, depending on the physiological duty cycle, communicate key data to a BCU or sink. The nodes that communicate data around the human body mostly transmit at low data rate with fixed packets size. Most nodes transmit periodically at a regular sampling frequency [16].

Depending on key criteria, nodes within a WBAN could transmit data on a non-periodic on-demand basis. For example, a health practitioner could demand live key physiological data from a specific sensor or group of sensors in response to a monitored condition. A WBAN could be used to monitor conditions such as an irregular heartbeat where constant monitoring is required over a period of time in order to identify the relevant physiological data.

Channel access methods for WBANs need to take account of the key QoS requirements whilst being as energy efficient as possible. Time Division Multiple Access (TDMA) is a channel access method that uses a frame and slot mechanism to divide the transmission time between nodes to allow multiple nodes to transmit. Contention-based schemes such as Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) monitor the status of the channel before transmitting packets. When a channel is free the node can transmit or back-off for a fixed period before transmitting. Under high traffic levels with low bandwidth, unslotted CSMA/CA can suffer from high levels of collision resulting in increased levels of latency and energy usage. Slotted CSMA/CA, as used in 802.15.4, via the use of superframes offers an alternative approach where the communications take place within a Guaranteed Time Slot (GTS). TDMA avoids collisions but the cost of this approach is the periodic node synchronization requirement to ensure all node clocks are aligned.

Contention-based protocols such as 802.15.4 have been widely accepted in WSNs. The benefits of low energy consumption due to nodes not needing to wake up to synchronize and scalability can outweigh the drawbacks of potential increases in packet collisions over TDMA in WSNs. High levels of contention within a WBAN environment will lead to unacceptable levels of retransmission and dropped packets. This could have serious implications if key physiological data was missed. However, the high levels of traffic that could lead to collisions in a WBAN should not be critical if the volume of traffic is kept low in relation to the network transmission capacity [16].

Traffic correlation is an important characteristic in a WBAN. Each sensor node has the task of collecting and correlating sensor data. However, the probability of sensor traffic load being affected by other closely coupled nodes due to a change in a physiological condition is high [10]. For example if a patient falls ill due to fever, his body temperature will rise together with heart rate and possibly BP. These changes may be reflected in the blood oxidization levels of the patient. This close coupling of sensor nodes and their resulting behaviors due to changing physiological conditions has an impact on channel traffic. Many nodes may initiate transmission requests at the same time resulting in a burst of data and a series of concurrent medium access requests.

3.5. Quality of Service

QoS is accepted as a measure of the service quality that the network offers to applications and users. Network reliability and guarantees of successful transmission are vital in WBANs due to the criticality of the data [17]. WBANs share many of the QoS issues that are inherent in WSNs: Resource constraints: energy, bandwidth, memory, limited transmission power; Unbalanced traffic: Potential n number of nodes communicating with a sink; Heterogeneous nodes: variety of sensor node types; and Packet criticality: priority traffic based on node criticality and condition type.

QoS levels within a WBAN environment take on new levels of criticality over and above those for WSNs due to the monitoring of key physiological life signs. Reliable transmission from sensor nodes to BCU and then potentially to remote healthcare professionals over HTTP or GPRS is vital and could be life threatening if not delivered with some guarantees. QoS requirements within a WBAN include high Packet Delivery Ratio (PDR), low levels of packet transmission delay, minimal collisions and retransmissions and efficient balancing of high energy efficiency and reliable transmission. Optimized energy efficiency requires an energy management process that efficiently balances QoS with energy efficiency, adapting to heterogeneous node constraints and wireless channel dynamics. QoS can only be evaluated at the higher layers of the protocol stack while energy consumption largely appears at the lower layers. The QoS for a WBAN solution can provide the core requirements guarantees for any network design where the various protocol stack parameters can be modified and adapted to deliver efficiencies as long as the QoS levels are still fulfilled.

3.6. Security

WBANs must incorporate adequate levels of security and privacy to protect the sensitive nature of personal and medical related data. WBANs are vulnerable to the same forms of attacks as other wireless networks such as eavesdropping and Denial of Service (DoS). These threats can expose a WBAN to serious security vulnerabilities and affect its performance at the physical, data link, network, and transport layers [18]. Patient data must be stored locally and securely transmitted to the server side in the context of transmitting data to a remote health professional. WBAN designers need to ensure that the processes for system setup and day to day operation do not introduce security risk as these tasks will be undertaken by non technical personnel. Efficient cryptographic techniques need to be used to mitigate the risk of security breaches but must not add significant overhead to packet size or transmission times.

4.1. Introduction

The main challenge in designing standards for WBANs is the balance of QoS demands with the low power constraints of battery powered nodes. A number of low power standards have been used in WSN/WBAN research as well as for commercial applications, all of which go some way in partly satisfying the requirements for life signs monitoring. Some low power standards designed to support low power sensing have been adapted for healthcare applications, e.g., ZigBee, while others such as 802.15.6 have been designed specifically for WBANs and cater for personal entertainment applications as well as healthcare.

4.2. IEEE 802.15.4 and ZigBee

ZigBee [19] is a standard designed for low power, minimal cost and extended battery life applications. The standard incorporates an application layer to define an application framework to support a number of application profiles and a network layer to implement networking and security. ZigBee has adopted 802.15.4 as its physical and MAC layers to make a complete protocol stack implementation. A simplified ZigBee protocol stack is shown in Figure 4. The application profiles include a number of standards including Building Automation, Smart Energy, Home Automation and Healthcare. The Healthcare profile enables specific health monitoring applications to use the ZigBee protocol stack. 802.15.4 operates in three frequency bands: 868 MHz (Europe), 915 MHz (ISM USA) and 2.4 GHz (ISM worldwide). The 2.4 GHz ISM band has the maximum data rate of 250 kb/s operating across 16 channels. Modulation at 2.4 GHz uses Orthogonal Quadrature Phase Shift Keying (O-QPSK) where the digital data resides in the phase of the signal. 802.15.4 uses a spread spectrum method—Direct Sequence Spread Spectrum (DSSS) to improve receiver sensitivity and reduce effects of multipath. 802.15.4 uses the concept of a Fully Function Device (FFD) and a Reduced Function Device (RFD) as the node roles.

A FFD is normally connected to a wired power supply and is capable of performing all the tasks in the 802.15.4 standard and can communicate with any device in the network. A RFD is battery powered node that is deliberately designed with limited functionality to extend battery life. Channel access in 802.15.4 can operate in beacon enabled mode via the use of superframes (slotted CSMA/CA) or in non-beacon mode (unslotted CSMA/CA).

Research has shown [20–22], that although 802.15.4 has been proven as a suitable standard for multihop WSNs providing QoS and adequate data rates, it is not the best solution for low power communications in WBANs. The technology was not designed to support WBANs and any WBAN implementation using 802.15.4 will ultimately have to adapt to the compromises necessary to deliver an efficient implementation.

4.3. IEEE 802.15.1 Bluetooth Low Energy

Bluetooth Low Energy (BLE) [23] has been designed as a low power WSN standard to operate in the same 2.4 GHz range as classic Bluetooth but uses different channels and a lower data rate of 200 kb/s rather than the 1–3 Mbps achieved via classic Bluetooth. BLE does not support mesh networking and only supports one packet type. BLE uses DSSS as the spread spectrum method and supports three separate application profiles: Sporting, Consumer and Healthcare. BLE is not backward compatible with classic Bluetooth, but Bluetooth 4.0 dual mode capability supports a direct interface between the standards where the protocols share one physical radio and antenna. Due to the plethora of Bluetooth-enabled devices in the marketplace, BLE is likely to be adopted widely by device manufacturers as it is a “least effort” forward path to deliver low power functionality. Although the likely wide adoption of BLE as a low power WSN standard cannot be ignored, the standard does not support QoS and is not specifically designed for WBAN applications.

4.4. IEEE 802.15.6

The 802.15.6 standard [5] aims to support WBAN requirements via low complexity, low cost, low power, and reliable transmission with data rates up to 10 Mbps. The standard defines a MAC layer and several physical layers. It uses the standard ISM bands as well as approved frequency bands specific to national regularity authorities. The 802.15.6 task group seeks to establish a low power communication standard while minimizing the Specific Absorption Rate (SAR) across the range of human body types (fat, thin, male, female, etc.). The MAC layer can be defined as beacon or non-beacon enabled modes. In non-beacon mode, the frame can be allocated as “with” or “without” superframe boundaries. In beacon mode the channel is divided into superframe structures where each superframe is bounded by a beacon. Nodes contend for channel access using CSMA/CA or slotted Aloha. Extended Access Phase (EAP) is used for high priority emergency traffic and Random Access Phase (RAP) is used for regular non-emergency traffic.

The supported frequency bands are grouped into Medical Implanted Communications (MIC), Wireless Medical Telemetry Services (WMTS) and ISM. MIC and WMTS bandwidths are not suitable for high data rate applications. The ISM band shares the spectrum with other wireless standards, e.g., 802.11 and 802.15.4 as well as household and industrial devices therefore, can be subject to interference.

802.15.6 defines three physical layers, as shown in Figure 5: Narrowband (NB), Ultra Wideband (UWB) and Human Body Communication (HBC). The NB physical layer is responsible for Clear Channel Assessment (CCA), radio transceiver activation/deactivation and data transmission and reception. The Physical Protocol Data Unit (PPDU) frame of NB physical layer contains a Physical Layer Convergence Procedure (PLCP) preamble a PLCP header and a Physical Service Data Unit (PSDU). The NB physical layer does not support high data rate applications.

The UWB physical layer operates in both low and high frequency bands. The low band consists of three channels (1–3). Channel 2 (mandatory channel) has a central frequency of 3,993.6 MHz. The high band has eight channels (4–11) where channel 7 (mandatory) has a central frequency of 7,987.2 MHz. The UWB physical layer transceivers cater for low complexity and power levels similar to the MIC band [24]. The HBC physical layer operates in the 16 MHz and 27 MHz bands. Both bands are valid for the USA, Japan and Korea, whereas the 27 MHz band is approved for use in Europe. The HBC specification covers the entire protocol for WBAN including packet structure, modulation and preamble/SFD.

Although 802.15.6 has been specifically designed for WBAN applications, as the standard caters for medical and non-medical applications, it cannot deliver optimal low power performance for life signs monitoring applications. Timmons and Scanlon [25] developed MedMAC, a MAC protocol, for ultra low power WSN applications. In experiments where MedMAC was compared with 802.15.6, it was found that MedMAC displayed a marked improvement in energy consumption with an energy saving of between 25.6%–33.2% for packet rates ranging from 1–10 kb/s.

5.1. Introduction

This section outlines a number of protocols operating at different layers. Many of the schemes have been outlined in the literature as WSN-specific, but are included here as either they form an important part in the understanding of sensor networks generally, or have formed the basis of WBAN specific protocols.

The unique attributes of a WBAN and its close proximity to the human body (in the context of low energy efficient communications) mean there are specific implications at each layer of the protocol stack [16]. Protocols working at each of the layers need to contribute to low packet transmission delay, minimum jitter, extremely low levels of congestion, low energy usage, whilst fulfilling specific QoS demands.

5.2. Physical Layer

Communications in and around the human body highlight a unique set of challenges that are not present in other types of networks. At the physical layer, protocols need to take account of how the body reacts to RF communications and the complications therein. The characteristics of RF signals at the physical layer can be separated into signal propagation effects in and along the human body and how body movement has an impact on the transmission of RF signals.

5.3. MAC Layer

Extensive research has been carried out on the adaptation of the MAC layer for WSNs to improve channel management and extend battery life. Although many of the schemes designed for WSNs focus on issues relating to distributed low power ad hoc networks, some have the potential to deliver improvements in WBAN applications. MAC layer research specific to WBANs is a relatively recent research area and is still an active topic for protocol designers. The transmission of RF signals is a major overhead for the energy efficiency of a WBAN. The MAC layer controls the channel access, packet encoding, addressing and as such must achieve maximum energy efficiency and data throughput by the efficient management of these functions. Studies reveal [33] that energy wastage in existing MAC protocols occurs from four major sources: excessive collisions, control packet overheads, idle listening, and overhearing. All contribute to network delay and energy inefficiency. WBAN applications require MAC protocols that are energy efficient and able to deliver battery lifetimes of several months or in the case of implanted nodes-years. MAC duty cycling needs to deliver energy efficient channel access to reduce packet collisions and reduce the impacts of idle listening and overhearing.

5.4. Network Layer

In a traditional WSN context the network layer has to cater for numerous disparate nodes potentially spread over a wide geographical area. Network layer protocols make use of routing algorithms that use multi-hop to route packets to the destination node. In comparison to WSNs, WBAN nodes are mobile, likely to be clustered in specific areas and do not have the same extensive multi-hop requirement as WSNs. In comparison to WSNs, the bandwidth available to WBANs is limited, likely to be shared with other local ISM band devices and is subject to physical layer attributes such as interference, fading, and attenuation due to the specific characteristics of the human body. Existing WSN research at the network layer doesn't fully consider the range of heterogeneous nodes within a WBAN and are generally concerned with energy efficient routing via static nodes rather than the extensive body movements in a WBAN. The required routing algorithm complexity within WBANs is low in comparison to large-scale WSNs where nodes are potentially distributed across large-scale areas. However, the unique factors relating to communicating in and around the human body mean that network layer issues need to form an important part of WBAN protocol design.

The standard WBAN architecture of a number of heterogeneous nodes linked to a BCU or sink device often assumes single hop routing where all node communications are transmitted directly to the BCU. Zawowski et al. [30] looked at the potential of multi-hop in a UWB based WBAN with an energy comparison for a transmission from a node located on the back of a patient to a node on the chest. Results showed that a multi-hop strategy is recommended if the energy for pulse generation and reception is higher than a ten thousandth part of the energy for bit encoding and decoding. The criteria to use multi-hop depends on the ratio of the energy consumption needed for decoding/coding and receiving/generating a UWB pulse. In experiments to evaluate path loss models for human body parts (torso, back, arm, leg) Reusens et al. [31] and later Joseph et al. [52] attempted to identify whether single or multi-hop transmissions are preferable in a WBAN. Observations in both showed that for nodes furthest away from the sink, there is scope for additional energy saving as these nodes consume the most energy and will die first. However, in the multi-hop scenario, the nodes closest to the sink consumed more energy as these nodes need to forward the data received from nodes further away. Results showed that a smart combination of single and multi-hop routing could yield the optimum energy efficiency.

Implanted nodes as part of a WBAN can create a thermal effect due to the properties of RF signal communications in and around the human body. Specific Absorption Rate (SAR) is a measure of the rate at which radiation energy is absorbed by tissue per unit weight. Exposure to high levels of SAR could result in tissue damage. Tang et al. [53] proposed a Thermal Aware Routing Algorithm (TARA) that handled packet transmission in the presence of temperature hot spots by effectively routing round areas that exceeded temperature thresholds. Results showed it to be a safer routing solution whilst balancing transmission delay and less network congestion. Adaptive Least Temperature Routing (ALTR) [54] improves upon the performance of TARA by routing packets to the least highest temperature node. Least Total Route Temperature (LTRT) protocol [55] converts node temperatures into graph weights to generate minimum temperature routes. Since LTRT aims to send packets with the shortest hop counts, it prevents the entire network temperature from rising quickly. Results showed that packets required less hop counts in comparison with LTR and ALTR. Kamal et al. [56] proposed a lightweight rendezvous algorithm (LR) that divides nodes into small clusters and showed that LR generated lower levels of temperature than LTRT and ALTR.

The clustering of nodes, where energy consumption from communications with the sink is equally divided between each of the nodes in the cluster, has been shown to deliver performance improvements in WSNs and could have potential for WBANs. One of the first protocols to demonstrate the advantages of clustering was the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol [57]. LEACH introduces data fusion into the routing protocol to reduce the amount of information that is transmitted to the sink to deliver significant improvements when compared to conventional routing protocols. Qin et al. [58] highlighted that LEACH and many of its successors assumed that all nodes in the network can communicate with the sink directly at all times and that this can be inconsistent with the practical application. Qin et al. proposed a Balanced Energy Consumption and Cluster based Routing Protocol (BECCRP) that improved upon LEACH where it sets gateways to relay the data from the cluster heads to share the energy consumption equally at each node, thereby extending the overall network lifetime. The protocol however, did not consider the nodes location and distance from other nodes. The summary of the performance of network protocols for WBAN is shown in Table 4.

5.5. Transport Layer

Transport layer protocols operating in a WBAN need to offer reliable packet delivery from the node to the BCU and in the case of specific sensor queries or emergency sensor data requests, reliable data delivery from BCU to node. Another key function of a transport protocol is the management of congestion in the network. Efficient control of network congestion can conserve battery life and increase the efficiency of packet delivery. Traditional transport layer protocols such as Transmission Control Protocol (TCP) and User Datagram Protocol (UDP) are too heavyweight and complex for WBAN applications resulting in latency and excessive energy wastage for low power wireless networks. TCPs 3-way handshake process with its end-to-end ACK approach would result in increased delay, increased buffer storage demands at the node level and ultimately poor QoS levels. UDP, although a connectionless protocol, has no guaranteed levels of reliability and will simply drop packets with no potential for recovery, this would obviously be a serious problem for life signs monitoring via a WBAN.

The design of energy efficient transport protocols needs to take into account the diversity of applications, traffic characteristics and resource constraints (namely energy and fairness) amongst a number of heterogeneous nodes. Protocols need to provide end-to-end reliability, QoS in an energy efficient way and be evaluated using metrics such as Packet Loss Ratio (PLR), latency, and fairness. Therefore, transport protocols should have components for congestion control and loss recovery since these two components have a direct impact on energy efficiency, reliability, and QoS [59].

Examples of transport layer protocols include COngestion Detection and Avoidance (CODA) [60], Pump Slowly Fetch Quickly (PSFQ) [61] and Event to Sink Reliable Transport (ESRT) [62]. CODA is a congestion control protocol that provides local congestion mechanisms to provide both buffer occupancy level and channel load condition to indicate network congestion. PSFQ provides reliable transport from sink to sensor (so called reverse path). PSFQ relies on the pump slowly operation to limit congestion but congestion will occur as the node numbers increase. A design solution involving a combination of CODA and PSFQ could potentially provide both congestion control and reliability in low power sensor networks. ESRT aims to provide reliability and congestion control with minimum energy usage. Reliability is defined by the number of data packets originated by an event that is successfully received at the sink using the ESRT algorithm. The ESRT algorithm tracks the event reporting frequency (f) of the received packets and matches it with the required reliability metric. Congestion is managed by decreasing reporting frequency without affecting reliability.

5.6. Application Layer

The application layer for a WBAN includes the application running on the node that may be specific to the sensor type as well as management, security, synchronization, and query type functions. Any application specific QoS constraints, e.g., latency, are handled at this level together with any data compression and signal processing. Although extensive research has been undertaken into protocols operating within the lower layers for WSNs/WBANs, there is very little existing research for protocols operating at the application layer.

At the application layer system administrators can interact with the nodes by using a Sensor Management Protocol (SMP) [63]. A SMP enables the lower layers to transparently interface with the application layer to undertake key management tasks such as management of rules relating to attribute naming and clustering, time synchronization and authentication. Sensor Query and Data Dissemination Protocol (SQDDP) [63] provides interfaces to issue attribute based queries. Sensor Query and Tasking Language (SQTL) [64] provides a mechanism to interface with events generated by a sensor node.

Compression of data at the application layer is a vital process that consolidates the data contained within each packet therefore, requiring fewer transmissions resulting in energy savings. Distributed Source Coding (DSC) [65] is a widely referenced compression technique used in WSNs. Nodes located close to each other may sense values that do not differ significantly. Each node would therefore, encode its data using fewer bits than it ordinarily would, making it impossible to retrieve the measured value with information from only one node. However, when all measurements reach the sink node, the DSC technique can combine the sensors encoded data to retrieve the values measured by all the sensors with minimal coding error. This process effectively moves the computational complexity from node to sink, thus fewer bits are transmitted and energy is saved. Other techniques such as Data Fusion and Data Aggregation [66] are further examples of methods used to reduce transmission data and save energy in low power networks.

Compressed Sensing (CS) is a relatively new signal processing technique that holds great promise for leveraging greater efficiency in the compression and recovery of signal data [67]. CS theory asserts that sufficient signal data can be recovered from far fewer samples or measurements than traditional methods where the sensed data adheres to a number of key criteria. CS relies on two principles: sparsity—which relates to the signal of interest and incoherence—which relates to the sensing modality [68]. Efficient sampling and sensing protocols can be designed based on CS techniques to capture useful data content embedded in a sparse signal and then condense the raw data into a reduced data set. What is most remarkable about CS is that it will allow a sensor node to very efficiently capture data in a sparse signal without needing to comprehend that signal, then by using numerical optimization techniques, CS can reconstruct the full length signal from the small amount of collected data. CS is a very efficient and effective signal acquisition method that in the context of a WBAN, could sample the data from a sensor node at a low rate and later reconstruction from what appears to be an incomplete set of measurements at the sink. The complexity of CS in comparison to standard Nyquist rate based signal processing techniques is moved to the sink side. This process alleviates complexity and therefore, battery draining processing from the sensing node. CS is an active research area and could hold great potential for WBANs, but careful analysis of the recovered signal must form a vital part of any research to ensure no QoS thresholds are breached and the accuracy of patient life signs data is not compromised.

6.1. Introduction

Cross-layer design is the concept of merging two or more layers within the protocol stack to improve the efficiency of the interaction between the protocols. The Open Systems Interconnection (OSI) model is a protocol stack abstraction that was developed to standardize communications by developing the concept of logical layers. The protocols at each layer hide the complexity of the layer below and provide services to the layer above. The protocols that fit into this type of model do not need to be aware of other protocols in the layers above or below. This offers many advantages in terms of scalability, compatibility, and flexibility of network design.

Resource efficiency can be gained by exploring a more unified scheme that merges common layer protocol functionalities into a cross-layer module for resource constrained nodes. However this type of structure, whilst possessing many advantages, cannot offer a consistent one size fitting solution for all network design problems. Protocol schemes that move away from the traditional layered model, whilst identifying significant advantages, must counter these improvements in network performance with a balanced view of the potential drawbacks of scalability or compatibility that may be introduced with the implementation of cross-layer solutions. The interaction between the layers can be varied but the justification to move away from the traditional model must be based on key benefits with a careful assessment of any downsides.

6.2. Justification for Cross-Layer Design

There are a number of reasons why the traditional layered approach to network design has been questioned and many of these have direct relevance to the specific needs of WBANs:

6.3. Methodology

Most cross-layer schemes involve the communications between different adjacent or non-adjacent layers of the protocol stack to improve the performance in some way over the traditional layered approach. These performance gains can be in the form of energy efficiency, reduced contention, better routing decisions or improved reliability. Research has shown that cross-layer designs can have a beneficial effect on the performance of wireless networks [70–72]. Taking a step back and looking at the protocol stack as a whole can help to focus on new solutions to wireless communication issues that a cross-layer approach can alleviate. Srivastava et al. [73] examined cross-layer interactions and summarized the various proposals in terms of methods that violate the traditional layered architecture. The main categories studied were: Creation of new interfaces: schemes that connect lower and higher layers either up or down and schemes that create an iterative flow between the two connected layers in both directions. Merging of adjacent layers: two adjacent layers merge their functionality to create a union of services. Vertical calibration across layers: setting of parameters across the layers either at design or runtime. New abstraction that replaces the protocol stack: disposing of the traditional protocol stack and replacing it with a new method that performs the same functions but in a non-layered form.

7.1. Introduction

Cross-layer protocols design specific to WBANs is a relatively immature research area but has the potential to deliver greater efficiencies than single layer adaptation schemes. Cross-layer protocol research in the comparable WSN area is more established with many schemes able to demonstrate significant energy efficiency improvements via the optimization of two or more layers. Generally, cross-layer schemes can be divided into loosely coupled and tightly coupled designs. Loosely coupled protocol designs focus on adapting the parameters available at a lower layer to optimize the performance at a higher layer. For example channel conditions at the physical layer could be utilized at the network layer as part of an intelligent routing algorithm. In the loosely coupled approach the individual layers within the protocol stack remain, but the parameters normally accessible only to the next highest layer could be made available to higher layers to provide performance gains or better/more informed decision making at the upper layers. In the tightly coupled approach to cross-layer design, the different layers are optimized together to form one complete solution to an optimization problem. Performance gains for tightly coupled designs should be greater than loosely coupled as they do not incur the same stack communication overhead, however this may be at the expense of protocol transparency and maintenance. This section highlights the benefits of cross-layer interaction across specific layers and details some of the relevant cross-layer schemes and the benefits they deliver to low power wireless communications.

7.2. MAC and Physical Layer Schemes

As the physical and MAC layers are close to each other in terms of their protocol stack roles, many cross-layer schemes have adapted both layers as part of a jointly optimized scheme to deliver improvements to traditional protocols. In practice, protocols generally incorporate a closely coupled interaction between the lower parts of the MAC layer and the physical layer. Many physical layer techniques for transmission, modulation and spread methods can enhance performance at the physical layer by improvements in throughput, delay and collision management, but it's only at the MAC layer level that these performance gains can be best adapted and optimized. Therefore, a protocol that incorporates the cross-layer design at the MAC/physical level has the potential to deliver enhanced performance over the traditional separate layer structure.

A variety of factors can affect performance in a low power wireless network, such as channel properties, traffic load, energy management policy, etc. The time varying nature of the wireless channel can have a significant influence on node energy consumption where poor link quality can result in increased energy usage. The Channel Adaptive Energy Management (CAEM) protocol [81] utilizes the time varying property of the wireless link by exploiting the cross-layer interaction between physical and MAC layers to deliver performance gains of up to 30% when compared to traditional protocols. CAEM allows a node to dynamically adjust the data throughput by changing the amount of error protection incorporated by the node according to the current quality of the link, estimated bandwidth and traffic load. The protocol buffers the packet temporarily until the channel recovers to the required quality. The obvious overhead is the inherent latency and potential buffer overflow due to the temporary storing of packets. To avoid this the protocol incorporates a scheduling and queuing algorithm to ensure that every sensor can equally access the wireless channel under such a fluctuating environment, thus achieving a balance between energy efficiency and fairness.

Many link adaptation schemes use the Signal to Noise Ratio (SNR) as the only input from the physical layer. For frequency selective channels, however, the SNR alone does not adequately describe the channel quality. Cross-layer performance can be significantly improved if the link adaptation scheme is based on a more detailed description of the channel condition. An extended exchange of information between the MAC and physical layers is required to fully exploit the status of the current channel conditions [82]. Utilizing detailed knowledge of the actual channel condition that is already available at the physical layer as prediction of the packet error probability for the currently observed channel can be obtained and exploited to improve performance. At the physical layer the parameters transmit power, modulation, and coding rate have a direct impact on multiple access of nodes in wireless channels by affecting node interference. Local adaptation of these parameters to achieve a target Bit Error Rate (BER) restrains both routing and MAC decisions.

Kozat et al. [71] considered end-to-end QoS guarantees at the physical and MAC layers and proposed a cross-layer framework that highlighted the gains in closely integrating these two layers. QoS can be interpreted differently at each of the protocol layers. At the physical layer, QoS can be defined as an acceptable BER or Signal to Interference to Noise Ratio (SINR). At the MAC layer and higher layers, it is normally expressed in terms of minimum rate or maximum delay guarantees. A QoS guarantee that is explicitly based on both minimum rate requirements and maximum tolerable BER is a good compromise, but must be satisfied at minimal energy expenditure. Wang et al. [83] developed a cross-layer design for healthcare monitoring that combined two modules: AMC in the physical layer and two sleep modes in the MAC layer. Normally AMC is used to change the transmission rate at the physical layer, improve BER, or vary bandwidth. Time slotted and unslotted sleep modes were used in the MAC layer. Energy consumption was expressed by the product of power level and time slot length. Simulation results showed that the cross-layer scheme achieved minimal energy consumption, thereby extending node lifetimes, while the spectral efficiency in the whole SNR range was maximized.

7.3. Network and MAC Layer Schemes

The network layer is responsible for selecting the optimal routing path for data packets. Different routing decisions alter the set of links to be scheduled and thereby influence the performance of the MAC layer, e.g., if the routing protocol chooses flow paths that are closer to each other, the subsequently higher interference and potential contention in the network make it harder for the MAC to resolve the transmission conflicts. When QoS requirements are ignored and link costs that accurately quantify the energy consumption can be assigned, the network layer becomes the sole determinant of energy consumption [71]. These link costs, however, depend on the transmit power which is a function of decisions made at the MAC and physical layers.

In many areas of adaptive MAC protocol design: S-MAC [33], T-MAC [35], and DMAC [36], the research is focused on the adaptation of the MAC layer and ignores the inter-working between different layers thereby attempting to avoid collisions among neighbor nodes without considering any routing information from the network layer or traffic patterns from the application. The best routing path can be identified by extracting key information from the lower layers, such as traffic volume, link quality, and collision data, to enhance performance at the network layer [84–87]. It could be argued that integrating routing with MAC as part of a cross-layer jointly optimized solution makes sense as the two functions are very closely coupled and a case can be made for integrating the layers either as one complete module or two closely coupled modules in the same layer. If routing is not taken into account by the MAC layer, optimal performance can only be achieved locally [80].

Cui et al. [88] proposed that energy efficiency must be supported across all layers of the protocol stack through cross-layer design. The proposed scheme incorporated a variable length TDMA protocol where the slot length is optimally assigned depending on the network layer routing requirement while minimizing the energy consumption across the network. CoLaNet [89] considered characteristics of the application to make better routing path choices at the network layer and demonstrated energy savings over S-MAC. Safwati et al. [75] proposed schemes to utilize cross-layer interactions between the MAC and network layers where the MAC sublayer provides the network layer with information pertaining to successfully receiving a CTS or an ACK frame or failure to receive one. This allows the network layer to choose the route that minimizes the probability of error to achieve the most energy effective route.

Most cross-layer designs look at the integration of adjacent or non-adjacent layers to adapt the protocol stack to deliver efficiencies over the traditional stack architecture. Schemes that extend the optimizing techniques to layers of intermediate nodes can provide additional energy saving benefits [70]. The network layer of a source node could be made aware of the channel conditions in the lower layers of the nodes in the neighborhood as part of a multi-hop scenario. Nodes could detect congestion and interference at the MAC layer and immediately react to it at the network layer. Routing decisions could be better addressed as more knowledge about the MAC and physical layers of adjacent nodes is available. Ruzzelli et al. [76] proposed Timezone Coordinated Sleep Scheduling (TICOSS) protocol as a cross-layer energy efficient multi-hop protocol based on 802.15.4. The network was divided up into time zones where each one takes turn in the transmission where nodes in the farthest time zone start the transmission. In the next slot the farthest but one sends its data and so on until the sink is reached. The protocol mitigates the hidden node problem, provides configurable shortest path routing to the PAN coordinator and almost doubles node lifetime for high traffic scenarios compared to standard WSN protocols.

Any protocol operating at the network layer needs to resolve the inherent conflict between energy efficiency and throughput. Sleep Collect and Send Protocol (SCSP) [90] dynamically calculates the node sleep and data receive (collect) periods depending on the amount of incoming traffic. The network layer uses a modified version of ZigBee routing that is more tolerant to node failures. The MAC layer provides the list of neighbor nodes to the network layer, which in turn provides multiple forwarding choices to it. Therefore, the MAC protocol has the option to change to the next hop router during transmission if the message is not successfully transmitted after n number tries. SCSP saves energy consumption by switching between active and sleep periods by dynamically adapting them depending on the amount of received traffic. The protocol uses a simple but efficient routing protocol that does not need route maintenance or discovery and works jointly with the MAC layer to enhance its fault tolerance properties. Simulations show that SCSP extends the network lifetime and connectivity in comparison with 802.15.4.

As the channel quality can vary due to the changing characteristics inherent in the physical layer of a wireless network, resource at the MAC layer is therefore, variable [80]. These variations can result in fluctuations of link capacity and transmission since transmission rate is related to many factors, not just link quality. Joint optimization for rate control coupled with resource allocation and routing in the context of multichannel operation is a complex challenge for network/MAC designers.

7.4. Transport and MAC, Physical Layer Schemes

Due to energy constraints and protocol simplicity, some low power standards, e.g., ZigBee that overlays 802.15.4, offers only best effort end-to-end delivery of individual packets. This means only limited transport layer functions (packet fragmentation and re-assembly) at the application layer as there is no fragmentation support at the network layer. Other standards that are designed to underpin the higher protocol layers require transport layer functions that overlay a low power MAC/physical to implement a full protocol stack.

Typically for traditional wireless networks, the standard protocols operating at the transport layer are TCP—providing connection orientated services to the network, and UDP—providing connectionless services. Overlaying a transport layer protocol such as standard TCP or UDP over a WBAN/WSN structure can be problematic in that each has their own overheads that impact on the energy efficiency of low power networks. For example, transmitting IPv6 packets over 802.15.4 cannot take place without the inclusion of an additional transport layer between IPv6 and the 802.15.4 MAC layer that basically compresses the frame to meet the physical payload size required by the protocol. Standard UDP has no mechanism for reliable communication, therefore cannot be considered in the WBAN context, as there is no method to support QoS. Additionally, as UDP cannot control traffic rate at the transport layer. The only way the traffic can be managed is via connection admission control or end-to-end rate control. These schemes would need to be cross optimized with the physical layer to cater for the variable link capacity.

Standard TCP can manage congestion avoidance, but as it was originally designed for wired networks, it suffers from performance issues related to interpretation of BER, limited bandwidth, and assumptions that packet loss is mainly caused by congestion. Performance gains can be made by optimizing a congestion control algorithm utilizing physical layer parameters as part of a cross layer scheme.

As the wireless link is prone to varying channel quality, fading, and interference, transport layer protocols can deliver better performance when optimized to consider the varying link capacity [91]. Cross-layer design solutions operating at the transport and physical layers could, for example, report the channel condition from the physical layer as a new parameter to the transport layer that would enable a protocol to distinguish real congestion from packet loss to make better performance related decisions. There is a direct relationship between the physical/MAC layer and transport layer where adapted cross-layer protocols can deliver higher throughput and minimal end-to-end packet delay. Spectrum sensing mechanisms operating at the lower layers can mitigate the issues relating to neighbor node interference, allowing protocols to sense the spectrum and access the channel in an opportunistic way allowing the transport layer to make more informed decisions concerning congestion and collisions in delivering reliable communications [ 92].

7.5. Application and MAC, Physical Layer Schemes

Although there are numerous examples of cross-layer schemes involving the lower layers, generally, cross-layer solutions incorporating the application layer do not figure highly in the available literature. Many existing protocols operating at the application layer were designed for wired networks and do not perform optimally in the low power wireless context. Schemes that can perform application level adaptation based on information from lower layers could provide performance benefits in low power wireless networks. The application layer can communicate the applications' QoS needs, i.e., data reliability, delay tolerance and response time to other layers [93]. Additionally, information on channel conditions from the MAC/physical layers could be used by the application layer as part of a cross-layer solution to improve performance. Rahman et al. [94] developed an adaptive cross-layer mechanism to control congestion for real- and non-real time data flow to support QoS guarantees at the application layer. Priority was given to real time data in terms of delay and available link capacity. The scheme linked the QoS requirements at the application layer and the packet waiting time, collision resolution and packet transmission time metrics at the MAC layer. Correlation-based Collaborative MAC (CC-MAC) protocol [95] is a cross-layer solution incorporating the application and MAC layers. CC-MAC exploits the spatial correlation between nodes to reduce energy consumption without compromising the estimated reliability achieved at the sink. Experiments showed that CC-MAC delivered improved performance over S-MAC and T-MAC in terms of energy efficiency, packet drop rate, and latency.

In their work on a new energy saving MAC model, Otal et al. [47] developed a cross-layer solution incorporating a fuzzy rule scheduling algorithm and radio activation policy. The researchers present Distributed Queuing Body Area Network (DQBAN) MAC as an enhancement to 802.15.4 for a BSN that extends the research carried out by Lin and Campbell [96]. The research incorporates a fuzzy rule scheduler that optimizes the MAC layer to improve overall performance in terms of QoS and energy consumption. The protocol considered the node cross-layer constraints such as physical layer signal quality (SNR), system waiting time, and residual battery life to allocate slots within a superframe structure. Simulations covering homogeneous and heterogeneous scenarios demonstrated that DQBAN MAC can achieve higher reliabilities when compared to 802.15.4 whilst delivering specific latency demands and battery limitations.

As part of research that focused on the benefits of cross-layer optimization between application and MAC layers, Chatterjea and Havinga [97] illustrated how two examples of an adaptive MAC could benefit in exchanging information with the application layer. To solve the problems of rapidly draining power sources and congestion in the network, the research exploited the high degree of spatial correlation that exists between the readings of adjacent nodes in a densely deployed network by utilizing a distributed and self organizing scheduling algorithm. The scheduling algorithm prevented two adjacent nodes acting as correlating nodes simultaneously and increased the robustness and accuracy of the data by giving every node a chance to act as a correlating node. Although simulations showed energy savings of up to 42% when using the cross-layer information, the researchers recognized that disturbances in the lower layers could have a detrimental effect on the upper layers. In particular, changes in link quality may lead to a reduction in the efficiency of the algorithm owing to the continuous rearranging of the scheduling scheme. This highlights the need for the upper layers to be adequately insulated from the instability of the lower layers.

Gaudadri and Baheti [98] proposed an application and MAC cross-layer solution for packet loss mitigation for WBANs using CS based on ECG signals. The work addressed the issues of end-to-end packet losses due to excessive path loss, interference, handoffs, congestion, and system load. It was observed that when the signal contains redundancies, the packet losses can be treated as lossy compression performed by the channel. Lost packets are identified at the application layer via a sequence number field in the packet header of the lower layers. The research observed that reconstruction accuracy degrades gracefully as packet loss rate increases. ECG signals can be recovered with high fidelity even in high packet loss conditions with 99% heartbeat detection accuracy at packet loss rates of 20% with a constant latency of less than 2.5 s.

7.6. Tightly Coupled—Multi-Layer Cross-Layer Schemes

In a network with multiple nodes all contending for resources, if no arbitration is enforced, QoS constraints can be breached. The traditional solution to this problem is to use the transport layer to perform congestion control, find the best path via an effective routing algorithm at the network layer, and use of the MAC protocol to access the channel to give the best single hop performance [79]. However, this type of mechanism can never deliver optimal performance as the algorithms in the various layers are not optimized together. Joint optimization between the full protocol stack is the only way to deliver full network wide scheduling and congestion control.

Akyildiz et al. [91] presented a Cross-layer Module (XLM) that replaced the entire layered architecture by a single protocol where the objective is reliable communication with minimal energy consumption, adaptive communication, and local congestion avoidance. Lin and Shroff [99] developed a similar scheme studying the performance bounds of cross-layer congestion control. XLM is governed by the Initiative Determination concept where the cross-layer protocol performs receiver-based contention, local congestion control, and distributed duty cycle operation to deliver efficient and reliable communications. Each node has the freedom to decide on participating in communication and initiate transmission by sending an RTS packet to indicate that it has a packet to send. Upon receipt the node will decide whether to participate in the communication.

Results show that XLM outperforms the traditional layered protocol stack in terms of performance and implementation complexity. One of the major advantages of cross-layer design is implementation efficiency. In a traditional layered architecture, each layer has clear boundaries and packet overhead leading to computation delays due to the sequential handling of a packet through the protocol stack.

Cross-layer Protocol (XLP) [100] extends XLM and merges the functionalities of traditional MAC, routing, and congestion control into a unified cross-layer module by considering physical layer and channel effects, avoiding the need for end-to-end congestion control. A summary of the protocols discussed above is shown in Table 5.

7.7. Pragmatic Approach to Cross-Layer Design

Wired networks are effectively a set of well defined reliable communication links over a hard wired transmission medium. Wireless communications, however, are broadcast in nature, subject to interference and signal attenuation and are inherently unreliable, especially in the context of on body communications. The current layered protocol stack was developed with wired communication in mind and has been adapted for use in the wireless context as a best effort use of existing architecture [73]. The new modalities inherent in the wireless medium are not optimally catered for in the current layered protocol stack and inevitably require at least some level of cross-layer design to develop the best solution for wireless communications. Cross-layer designs usually improve performance but can do so at the expense of higher complexity in communication and computation, making complexity reduction an important issue. Kawadia and Kumar [101] argued that layered architecture can be optimal in most cases. Cross-layer design can potentially work at cross purposes and if designers are not careful, a negative effect on system performance is possible [79]. Issues that cross-layer design can introduce are:

Efficient Low Power Protocols

Protocols that deliver extremely high levels of energy efficiency are required for successful WBAN implementations. The focus on this main challenge is likely to remain a key area of ongoing research due to the limitations of energy supply and longevity requirements especially for embedded nodes. Context aware protocols that differentiate between steady state and emergency traffic provide an interesting insight but further research is needed to move this area forward form the modeling/simulation arena to real life practical applications [48,50].

Extended Battery Lifetime

The potential of micro fuel cells and nanowire battery technology could result in significant breakthroughs for WBAN architectures. The resulting increase in density could significantly extend battery lifetimes over existing lithium based power supplies. The reduced size of these new battery technologies will result in a smaller sensor node footprint as the battery is currently a major contributor to node size.

Security

It is vital that the key personal data that is communicated via WBANs is protected to ensure its integrity is maintained. However, complex communication protocols are expensive in terms of computational effort and, therefore, energy usage. Future research in this area needs to identify adequate levels of information security whilst not compromising on energy efficiency.

Nanotechnology based Sensors

Recent innovations in nanotechnology show that the development of tiny fully integrated sensor devices that include densely packed batteries, bio-sensors and nano-scale RF transmitters is now a reality. The mechanisms for communicating with these devices may require new approaches requiring new protocols to facilitate communications at the nano-scale.

Energy Harvesting

The limitations of battery life are a key consideration in the design of efficient WBAN protocols. Extending battery lifetime via the harvesting of available energy resources can be a valuable method in extending the operation of WBAN sensors. The human body emits heat and also vibration during movement. These sources of energy could be scavenged as part of an energy efficient protocol implementation to extend network life.

User Experience and operational Issues

Much of the existing research has focused on the technical elements of sensing and efficiently communicating data within a WBAN. However, for systems to gain acceptance in the user community, designers will need to cater for the requirements of a wide range of real life scenarios that do not currently feature heavily in the available research. This will include the ability of systems to operate efficiently within extreme environments, in areas with high levels of interference, and that are able to adapt and perform in alignment with peoples lifestyles.

Integration of WBAN architectures and the Internet

The integration of WBAN specific low power protocols and IPv6 is key to the seamless working of systems using these technologies. Future research will need to focus on tighter integration and interoperability between these two areas to ensure efficient communications from low power network to the Internet.