A Review of Protocol Implementations and Energy Efficient Cross-Layer Design for Wireless Body Area Networks <s> Application Layer <s> This paper describes the concept of sensor networks which has been made viable by the convergence of micro-electro-mechanical systems technology, wireless communications and digital electronics. First, the sensing tasks and the potential sensor networks applications are explored, and a review of factors influencing the design of sensor networks is provided. Then, the communication architecture for sensor networks is outlined, and the algorithms and protocols developed for each layer in the literature are explored. Open research issues for the realization of sensor networks are also discussed. <s> BIB001 </s> A Review of Protocol Implementations and Energy Efficient Cross-Layer Design for Wireless Body Area Networks <s> Application Layer <s> A fundamental challenge in the design of wireless sensor networks (WSNs) is to maximize their lifetimes. Data aggregation has emerged as a basic approach in WSNs in order to reduce the number of transmissions of sensor nodes, and hence minimizing the overall power consumption in the network. We study optimal data aggregation in WSNs. Data aggregation is affected by several factors, such as the placement of aggregation points, the aggregation function, and the density of sensors in the network. The determination of an optimal selection of aggregation points is thus extremely important. We present exact and approximate algorithms to find the minimum number of aggregation points in order to maximize the network lifetime. Our algorithms use a fixed virtual wireless backbone that is built on top of the physical topology. We also study the tradeoffs between energy savings and the potential delay involved in the data aggregation process. Numerical results show that our approach provides substantial energy savings. <s> BIB002 </s> A Review of Protocol Implementations and Energy Efficient Cross-Layer Design for Wireless Body Area Networks <s> Application Layer <s> Conventional approaches to sampling signals or images follow Shannon's theorem: the sampling rate must be at least twice the maximum frequency present in the signal (Nyquist rate). In the field of data conversion, standard analog-to-digital converter (ADC) technology implements the usual quantized Shannon representation - the signal is uniformly sampled at or above the Nyquist rate. This article surveys the theory of compressive sampling, also known as compressed sensing or CS, a novel sensing/sampling paradigm that goes against the common wisdom in data acquisition. CS theory asserts that one can recover certain signals and images from far fewer samples or measurements than traditional methods use. <s> BIB003 </s> A Review of Protocol Implementations and Energy Efficient Cross-Layer Design for Wireless Body Area Networks <s> Application Layer <s> The emerging compressed sensing (CS) theory can significantly reduce the number of sampling points that directly corresponds to the volume of data collected, which means that part of the redundant data is never acquired. It makes it possible to create standalone and net-centric applications with fewer resources required in Internet of Things (IoT). CS-based signal and information acquisition/compression paradigm combines the nonlinear reconstruction algorithm and random sampling on a sparse basis that provides a promising approach to compress signal and data in information systems. This paper investigates how CS can provide new insights into data sampling and acquisition in wireless sensor networks and IoT. First, we briefly introduce the CS theory with respect to the sampling and transmission coordination during the network lifetime through providing a compressed sampling process with low computation costs. Then, a CS-based framework is proposed for IoT, in which the end nodes measure, transmit, and store the sampled data in the framework. Then, an efficient cluster-sparse reconstruction algorithm is proposed for in-network compression aiming at more accurate data reconstruction and lower energy efficiency. Performance is evaluated with respect to network size using datasets acquired by a real-life deployment. <s> BIB004
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) BIB001 . 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) BIB001 provides interfaces to issue attribute based queries. Sensor Query and Tasking Language (SQTL)  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)  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 BIB002 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 BIB004 . 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 BIB003 . 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.