Congestion control protocols in wireless sensor networks: A survey

The performance of wireless sensor networks (WSN) is affected by the lossy communication medium, application diversity, dense deployment, limited processing power and storage capacity, frequent topology change. All these limitations provide significant and unique design challenges to data transport control in wireless sensor networks. An effective transport protocol should consider reliable message delivery, energy-efficiency, quality of service and congestion control. The latter is vital for achieving a high throughput and a long network lifetime. Despite the huge number of protocols proposed in the literature, congestion control in WSN remains challenging. A review and taxonomy of the state-of-the-art protocols from the literature up to 2013 is provided in this paper. First, depending on the control policy, the protocols are divided into resource control vs. traffic control. Traffic control protocols are either reactive or preventive (avoiding). Reactive solutions are classified following the reaction scale, while preventive solutions are split up into buffer limitation vs. interference control. Resource control protocols are classified according to the type of resource to be tuned. © 2014 IEEE

TRASA: TRaffic Aware Slot Assignment Algorithm in Wireless Sensor Networks

International audienceIn data gathering applications which is a typical application paradigm in wireless sensor networks, sensor nodes may have different traffic demands. Assigning equal channel access to each node may lead to congestion, inefficient use of the bandwidth and decrease of the application performance. In this paper, we prove that the time slot assignment problem is NP-complete when p-hop nodes are not assigned the same slot, with 1 <= p <= h for any strictly positive integer h. We propose TRASA, a TRaffic Aware time Slot Assignment algorithm able to allocate slots to sensors proportionally to their demand. We evaluate the performance of TRASA for different heuristics and prove that it provides an optimized spatial reuse and a minimized cycle length

Data Aggregation and Cross-layer Design in WSNs

Over the past few years, advances in electrical engineering have allowed electronic devices to shrink in both size and cost. It has become possible to incorporate environmental sensors into a single device with a microprocessor and memory to interpret the data and wireless transceivers to communicate the data. These sensor nodes have become small and cheap enough that they can be distributed in very large numbers into the area to be monitored and can be considered disposable. Once deployed, these sensor nodes should be able to self-organize themselves into a usable network. These wireless sensor networks, or WSNs, differ from other ad hoc networks mainly in the way that they are used. For example, in ad hoc networks of personal computers, messages are addressed from one PC to another. If a message cannot be routed, the network has failed. In WSNs, data about the environment is requested by the data sink. If any or multiple sensor nodes can return an informative response to this request, the network has succeeded. A network that is viewed in terms of the data it can deliver as opposed to the individual devices that make it up has been termed a data-centric network [26]. The individual sensor nodes may fail to respond to a query, or even die, as long as the final result is valid. The network is only considered useless when no usable data can be delivered. In this thesis, we focus on two aspects. The first is data aggregation with accurate timing control. In order to maintain a certain degree of service quality and a reasonable system lifetime, energy needs to be optimized at every stage of system operation. Because wireless communication consumes a major amount of the limited battery power for these sensor nodes, we propose to limit the amount of data transmitted by combining redundant and complimentary data as much as possible in order to transmit smaller and fewer messages. By using mathematical models and computer simulations, we will show that our aggregation-focused protocol does, indeed, extend system lifetime. Our secondary focus is a study of cross-layer design. We argue that the extremely specialized use of WSNs should convince us not to adhere to the traditional OSI networking model. Through our experiments, we will show that significant energy savings are possible when a custom cross-layer communication model is used

A Utility-Based Approach to Bandwidth Allocation and Link Scheduling in Wireless Networks

We study the problem of optimizing aggregate user utility in wireless ad-hoc networks under the constraints of wireless interference. We develop a market-oriented approach to bandwidth allocation with a tˆatonnement process and demonstrate its ability to effectively price bottleneck resource. One novelty is that we choose to price “interference goods” to capture the externality imposed by one application’s use of the network on other applications. In making progress we also propose a modification to the CSMA protocol that is robust enough to handle a non-schedulable bandwidth schedule. Experimental results on simulated network topologies show that the market-based approach has better scalability than alternate approximation methods and is much more efficient in terms of runtime.Engineering and Applied Science

WSN-DS: A Dataset for Intrusion Detection Systems in Wireless Sensor Networks

Wireless Sensor Networks (WSN) have become increasingly one of the hottest research areas in computer science due to their wide range of applications including critical military and civilian applications. Such applications have created various security threats, especially in unattended environments. To ensure the security and dependability of WSN services, an Intrusion Detection System (IDS) should be in place. This IDS has to be compatible with the characteristics of WSNs and capable of detecting the largest possible number of security threats. In this paper a specialized dataset for WSN is developed to help better detect and classify four types of Denial of Service (DoS) attacks: Blackhole, Grayhole, Flooding, and Scheduling attacks. This paper considers the use of LEACH protocol which is one of the most popular hierarchical routing protocols in WSNs. A scheme has been defined to collect data from Network Simulator 2 (NS-2) and then processed to produce 23 features. The collected dataset is called WSN-DS. Artificial Neural Network (ANN) has been trained on the dataset to detect and classify different DoS attacks. The results show that WSN-DS improved the ability of IDS to achieve higher classification accuracy rate. WEKA toolbox was used with holdout and 10-Fold Cross Validation methods. The best results were achieved with 10-Fold Cross Validation with one hidden layer. The classification accuracies of attacks were 92.8%, 99.4%, 92.2%, 75.6%, and 99.8% for Blackhole, Flooding, Scheduling, and Grayhole attacks, in addition to the normal case (without attacks), respectively

Deployment challenges and developments in wireless sensor networks clustering

Clustering techniques for wireless sensor networks (WSNs) have been extensively studied and proven to improve the network lifetime, a primary metric, used for performance evaluation of sensor networks. Although introduction of clustering techniques has the potential to reduce energy consumption and extend the lifetime of the network by decreasing the contention through either power control or node scheduling, scalability remains an issue. Therefore, the optimality of the cluster size still needs to be thoroughly investigated. In this paper, a single cluster head (CH) queuing model is presented. Using an event based simulation tool (Castalia), key issues that affect the practical deployment of clustering techniques in wireless sensor networks are analysed. These include identifying the bottlenecks in terms of cluster scalability and predicting the nature of data packets arrival distribution at the CH. Results presented show that this analysis can be used to specify the size of a cluster, when a specific flow of data is expected from the sensing nodes based on a particular application and also the distribution of the inter-arrival times of data packets at the CH follows exponential distribution

Energy-efficient data acquisition for accurate signal estimation in wireless sensor networks

Long-term monitoring of an environment is a fundamental requirement for most wireless sensor networks. Owing to the fact that the sensor nodes have limited energy budget, prolonging their lifetime is essential in order to permit long-term monitoring. Furthermore, many applications require sensor nodes to obtain an accurate estimation of a point-source signal (for example, an animal call or seismic activity). Commonly, multiple sensor nodes simultaneously sample and then cooperate to estimate the event signal. The selection of cooperation nodes is important to reduce the estimation error while conserving the network’s energy. In this paper, we present a novel method for sensor data acquisition and signal estimation, which considers estimation accuracy, energy conservation, and energy balance. The method, using a concept of ‘virtual clusters,’ forms groups of sensor nodes with the same spatial and temporal properties. Two algorithms are used to provide functionality. The ‘distributed formation’ algorithm automatically forms and classifies the virtual clusters. The ‘round robin sample scheme’ schedules the virtual clusters to sample the event signals in turn. The estimation error and the energy consumption of the method, when used with a generalized sensing model, are evaluated through analysis and simulation. The results show that this method can achieve an improved signal estimation while reducing and balancing energy consumption

Clustering objectives in wireless sensor networks: A survey and research direction analysis

Wireless Sensor Networks (WSNs) typically include thousands of resource-constrained sensors to monitor their surroundings, collect data, and transfer it to remote servers for further processing. Although WSNs are considered highly flexible ad-hoc networks, network management has been a fundamental challenge in these types of net- works given the deployment size and the associated quality concerns such as resource management, scalability, and reliability. Topology management is considered a viable technique to address these concerns. Clustering is the most well-known topology management method in WSNs, grouping nodes to manage them and/or executing various tasks in a distributed manner, such as resource management. Although clustering techniques are mainly known to improve energy consumption, there are various quality-driven objectives that can be realized through clustering. In this paper, we review comprehensively existing WSN clustering techniques, their objectives and the network properties supported by those techniques. After refining more than 500 clustering techniques, we extract about 215 of them as the most important ones, which we further review, catergorize and classify based on clustering objectives and also the network properties such as mobility and heterogeneity. In addition, statistics are provided based on the chosen metrics, providing highly useful insights into the design of clustering techniques in WSNs.publishedVersio

Optimizing Transmission and Shutdown for Energy-Efficient Packet Scheduling in Sensor Networks

Energy-efficiency is imperative to enable the deployment of sensor networks with satisfactory lifetime. Conventional power management in radio communication primarily focuses independently on the physical layer, medium access control (MAC) or routing and approaches differ depending on the levels of abstraction. At the physical layer, the fundamental trade-off that exists between transmission rate and energy is exploited. This leads to the lazy scheduling approach, which consists of transmitting with the lowest power over the longest feasible duration. At MAC level, power reduction techniques tend to keep the transmission as short as possible to maximize the radio\u27s power-off interval. Those two approaches seem conflicting and it is not clear which one is the most appropriate for a given network scenario. In this paper, we propose a transmission strategy that combines both techniques optimally. We present a cross-layer solution to determine the best transmission strategy taking into account the transceiver power consumption characteristics, the system load and the scenario constraints. Based on this approach, we derive a low complexity, on-line scheduling algorithm that can be used to optimally organize the forwarding of the sensed information from cluster heads to the data sink (uplink) in a hierarchical sensor network. Results, considering Coded Frequency Shift Keying (FSK) modulation, show that depending on the scenario, a 50% extra power reduction is achieved in a realistic uplink data gathering context, compared to the case where only transmission rate scaling or shutdown is considered