A Review of Protocol Implementations and Energy Efficient Cross-Layer Design for Wireless Body Area Networks <s> Transport Layer <s> There is a growing need to support reliable data communications in sensor networks that are capable of supporting new applications, such as, assured delivery of high-priority events to sinks, reliable control and management of sensor networks, and remotely programming/retasking sensor nodes over-the-air. We present the design, implementation, and evaluation of pump slowly, fetch quickly (PSFQ), a simple, scalable, and robust transport protocol that is customizable to meet the needs of emerging reliable data applications in sensor networks. PSFQ represents a simple approach because it makes minimum assumptions about the underlying routing infrastructure, it is scalable and energy- efficient because it supports minimum signaling, thereby reducing the communication cost for data reliability, and importantly, it is robust because it is responsive to a wide range of operational error conditions found in sensor network, allowing for the successful operation of the protocol even under highly error-prone conditions. The key idea that underpins the design of PSFQ is to distribute data from a source node by pacing data at a relatively slow speed ("pump slowly"), but allowing nodes that experience data loss to fetch (i.e., recover) any missing segments from their local immediate neighbors aggressively ("fetch quickly"). We present the design and implementation of PSFQ, and evaluate the protocol using the ns-2 simulator and an experimental wireless sensor testbed based on Berkeley motes and the TinyOS operating system. We show that PSFQ can outperform existing related techniques and is highly responsive to the various error conditions experienced in sensor networks. The source code for PSFQ is freely available for experimentation. <s> BIB001 </s> A Review of Protocol Implementations and Energy Efficient Cross-Layer Design for Wireless Body Area Networks <s> Transport Layer <s> Wireless sensor networks (WSNs) are event-based systems that rely on the collective effort of several microsensor nodes. Reliable event detection at the sink is based on collective information provided by source nodes and not on any individual report. However, conventional end-to-end reliability definitions and solutions are inapplicable in the WSN regime and would only lead to a waste of scarce sensor resources. Hence, the WSN paradigm necessitates a collective event-to-sink reliability notion rather than the traditional end-to-end notion. To the best of our knowledge, reliable transport in WSN has not been studied from this perspective before. In order to address this need, a new reliable transport scheme for WSN, the event-to-sink reliable transport (ESRT) protocol, is presented in this paper. ESRT is a novel transport solution developed to achieve reliable event detection in WSN with minimum energy expenditure. It includes a congestion control component that serves the dual purpose of achieving reliability and conserving energy. Importantly, the algorithms of ESRT mainly run on the sink, with minimal functionality required at resource constrained sensor nodes. ESRT protocol operation is determined by the current network state based on the reliability achieved and congestion condition in the network. This self-configuring nature of ESRT makes it robust to random, dynamic topology in WSN. Furthermore, ESRT can also accommodate multiple concurrent event occurrences in a wireless sensor field. Analytical performance evaluation and simulation results show that ESRT converges to the desired reliability with minimum energy expenditure, starting from any initial network state. <s> BIB002 </s> A Review of Protocol Implementations and Energy Efficient Cross-Layer Design for Wireless Body Area Networks <s> Transport Layer <s> Event-driven sensor networks operate under an idle or light load and then suddenly become active in response to a detected or monitored event. The transport of event impulses is likely to lead to varying degrees of congestion in the network depending on the distribution and rate of packet sources in the network. It is during these periods of event impulses that the likelihood of congestion is greatest and the information in transit of most importance to users. To address this challenge we propose an energy-efficient congestion control scheme for sensor networks called CODA (COngestion Detection and Avoidance) that comprises three mechanisms: (i) receiver-based congestion detection; (ii) open-loop hop-by-hop backpressure; and (iii) closed-loop multisource regulation. We present the detailed design, implementation, and evaluation of CODA using simulation and experimentation. We define three important performance metrics (i.e., energy tax, fidelity penalty, and power) to evaluate the impact of CODA on the performance of sensing applications. We discuss the performance benefits and practical engineering challenges of implementing CODA in an experimental sensor network testbed based on Berkeley motes using CSMA. Simulation results indicate that CODA significantly improves the performance of data dissemination applications such as directed diffusion by mitigating hotspots, and reducing the energy tax and fidelity penalty on sensing applications. We also demonstrate that CODA is capable of responding to a number of congestion scenarios that we believe will be prevalent as the deployment of these networks accelerates. <s> BIB003
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  . Examples of transport layer protocols include COngestion Detection and Avoidance (CODA) BIB003 , Pump Slowly Fetch Quickly (PSFQ) BIB001 and Event to Sink Reliable Transport (ESRT) BIB002 . 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.