Geographic routing resilient to location errors

Geographic routing is an attractive option for large scale wireless sensor networks (WSNs) because of its low overhead and energy expenditure, but is inefficient in realistic localization conditions. Positioning systems are inevitably imprecise because of inexact range measurements and location errors lead to poor performance of geographic routing in terms of packet delivery ratio (PDR) and energy efficiency. This paper proposes a novel, low-complexity, error-resilient geographic routing method, named conditioned mean square error ratio (CMSER) routing, intended to efficiently make use of existing network information and to successfully route packets when localization is inaccurate. Next hop selection is based on the largest distance to destination (minimizing the number of forwarding hops) and on the smallest estimated error figure associated with the measured neighbor coordinates. It is found that CMSER outperforms other basic greedy forwarding techniques employed by algorithms such as most forward within range (MFR), maximum expectation progress (MEP) and least expected distance (LED). Simulation results show that the throughput for CMSER is higher than for other methods, additionally it also reduces the energy wasted on lost packets by keeping their routing paths short

Enhanced hybrid positioning in wireless networks I: AoA-ToA

Localization in wireless networks presents enormous challenges for scientists and engineers. Some of the most commonly used techniques for localization are based on time of arrival (ToA), received signal strength (RSS) and angle of arrival (AoA) of the signals. In this paper we analyze and propose improvements to the location accuracy of hybrid (AoA-ToA) localization systems. The location coordinates are obtained using a linear least squares (LLS) algorithm. A closed form expression for the mean square error (MSE) of the LLS estimator is derived. Furthermore, the information present in the covariance of the incoming signals is utilized and a novel weighted linear least squares (WLLS) method is proposed. It is shown via simulation that the theoretical MSE accurately predicts the performance of the LLS estimator. It is also shown via simulation that the WLLS algorithm exhibits better performance than the LLS algorithm

Optimized hybrid localisation with cooperation in wireless sensor networks

In this study, the authors introduce a novel hybrid cooperative localisation scheme when both distance and angle measurements are available. Two linear least squares (LLS) hybrid cooperative schemes based on angle of arrival–time of arrival (AoA–ToA) and AoA–received signal strength (AoA–RSS) signals are proposed. The proposed algorithms are modified to accommodate cooperative localisation in resource constrained networks where only distance measurements are available between target sensors (TSs) while both distance and angle measurements are available between reference sensors and TSs. Furthermore, an optimised version of the LLS estimator is proposed to further enhance the localisation performance. Moreover, localisation of sensor nodes in networks with limited connectivity (partially connected networks) is also investigated. Finally, computational complexity analysis of the proposed algorithms is presented. Through simulation, the superior performance of the proposed algorithms over its non-cooperative counterpart and the hybrid signal based iterative non-linear least squares algorithms is demonstrated

Cooperative positioning using angle of arrival and time of arrival

Localization has been one of the most highly researched topics in wireless communications in the past decade. Localization of wireless nodes can be achieved using a variety of techniques, in which range measurement and angle measurement are most commonly used. In the presence of both angle and range measurement, a hybrid model can be developed. In this paper we analyze a hybrid angle of arrival-time of arrival (AoA-ToA) model for localization of wireless nodes, the model is modified to remove the bias from the estimated positions. We also explore the idea of cooperative localization using both angle and range measurements and develop a linear least squares (LLS) scheme. It is shown via simulation that the modified model is unbiased and that the performance of the proposed cooperative LLS is superior to its non-cooperative counterpart

Enhanced hybrid positioning in wireless networks II: AoA-RSS

In order to achieve higher location estimation accuracy through utilizing all the available information, in this paper we propose a hybrid localization system. We use the angle of arrival (AoA) measurement with the inherent received signal strength (RSS) information to develop an AoA-RSS linear least squares (LLS) location estimator. To accurately predict the performance of the LLS estimator, a closed form expression for the mean square error (MSE) is also derived. Furthermore, the information present in the covariance of the incoming signals is utilized and a novel weighted linear least squares (WLLS) method is proposed. It is shown via simulation that the theoretical MSE accurately predicts the performance of the LLS estimator. It is also shown via simulation that the WLLS algorithm exhibits better performance than the LLS algorithm

Localization of multiple nodes based on correlated measurements and shrinkage estimation

Accurate covariance matrix estimation has applications in a wide range of disciplines. For many applications the estimated covariance matrix needs to be positive definite and hence invertible. When the number of data points is insufficient, the estimated sample covariance matrix has two fold disadvantages. Firstly, although it is unbiased, it consists of a large estimation error. Secondly, it is not positive definite. A shrinkage technique has been proposed in the fields of finance and life sciences to estimate the covariance matrix that is invertible and contains relatively a small estimation error variance. In this paper, we introduce the shrinkage covariance matrix concept in the area of multiple target localization in wireless networks with correlated measurements. For localization, we use the low cost received signal strength (RSS) measurements. Unlike most studies, where the links between sensor nodes (SNs) and targets nodes (TNs) are independent, we use a realistic model where these links are correlated. Optimization in location accuracy is achieved by weighting each link via the shrinkage covariance matrix. Simulation results show that using the estimated shrinkage covariance improves the location accuracy of the localization algorithm

Impact of mobility on the IoT MAC infrastructure: IEEE 802.15.4e TSCH and LLDN platform

Realizing the target of high reliability and availability is a crucial concept in the IoT context. Different types of IoT applications introduce several requirements and obstacles. One of the important aspects degrading network performance is the node mobility inside the network. Without a solid and adaptive mechanism, node mobility can disrupt the network performance due to dissociations from the network. Hence, reliable techniques must be incorporated to tackle the overhead of node movement. In this paper, the overhead of mobility on both IEEE 802.15.4e timeslotted channel hopping (TSCH) and low latency deterministic (LLDN) modes is investigated. These two modes can be considered as the MAC layer of the IoT paradigm because of their importance and resilience to different network obstacles. In addition, the set of metrics and limitations that influence the network survivability will be identified to ensure efficient mobile node handling process. Both TSCH and LLDN have been implemented via the Contiki OS to determine their functionality. TSCH has been demonstrated to have better node connectivity due to the impact of frame collision in LLDN. In addition, by neglecting the overhead of collision, the LLDN has been shown to have better connectivity and low radio duty cycle (RDC)

A Game Theoretic Optimization of RPL for Mobile Internet of Things Applications

The presence of mobile nodes in any wireless network can affect the performance of the network, leading to higher packet loss and increased energy consumption. However, many recent applications require the support of mobility and an efficient approach to handle mobile nodes is essential. In this paper, a game scenario is formulated where nodes compete for network resources in a selfish manner, to send their data packets to the sink node. Each node counts as a player in the noncooperative game. The optimal solution for the game is found using the unique Nash equilibrium (NE) where a node cannot improve its pay-off function while other players use their current strategy. The proposed solution aims to present a strategy to control different parameters of mobile nodes (or static nodes in a mobile environment) including transmission rate, timers and operation mode in order to optimize the performance of RPL under mobility in terms of packet delivery ratio (PDR), throughput, energy consumption and end-to-end-delay. The proposed solution monitors the mobility of nodes based on received signal strength indication (RSSI) readings, it also takes into account the priorities of different nodes and the current level of noise in order to select the preferred transmission rate. An optimized protocol called game-theory based mobile RPL (GTM-RPL) is implemented and tested in multiple scenarios with different network requirements for Internet of Things applications. Simulation results show that in the presence of mobility, GTM-RPL provides a flexible and adaptable solution that improves throughput whilst maintaining lower energy consumption showing more than 10% improvement compared to related work. For applications with high throughput requirements, GTM-RPL shows a significant advantage with more than 16% improvement in throughput and 20% improvement in energy consumption

Dynamic cluster head election protocol for mobile wireless sensor networks

A dynamic cluster head election protocol (DCHEP) is proposed in this work to improve network availability and energy efficiency for mobile wireless sensor networks (WSNs) under the beacon-enabled IEEE 802.15.4 standard. The proposed protocol (DCHEP) is developed and simulated using CASTALIA/OMNET++ with a realistic radio model and node behaviour. DCHEP improves the network availability and lifetime and maintains clusters hierarchy in a proactive manner even in a mobile WSN where all the nodes including cluster heads (CHs) are mobile, this is done by dynamically switching CHs allowing nodes to act as multiple backup cluster heads (BCHs) with different priorities based on their residual energy and connectivity to other clusters. DCHEP is a flexible and scalable solution targeted for dense WSNs with random mobility. The proposed protocol achieves an average of 33% and 26% improvement to the availability and energy efficiency respectively compared with the original standard

Congestion Control for 6LoWPAN Networks: A Game Theoretic Framework

The Internet of Things (IoT) has been considered as an emerging research area where the 6LoWPAN (IPv6 over Low-Power Wireless Personal Area Network) protocol stack is considered as one of the most important protocol suite for the IoT. Recently, the Internet Engineering Task Force has developed a set of IPv6 based protocols to alleviate the challenges of connecting resource limited sensor nodes to the Internet. In 6LoWPAN networks, heavy network traffic causes congestion which significantly degrades network performance and effects the quality of service (QoS) aspects e.g. throughput, end-to-end delay and energy consumption. In this paper, we formulate the congestion problem as a non-cooperative game framework where the nodes (players) behave uncooperatively and demand high data rate in a selfish way. Then, the existence and uniqueness of Nash equilibrium is proved and the optimal game solution is computed by using Lagrange multipliers and KKT conditions. Based on this framework, we propose a novel and simple congestion control mechanism called game theory based congestion control framework (GTCCF) specially tailored for IEEE 802.15.4, 6LoWPAN networks. GTCCF is aware of node priorities and application priorities to support the IoT application requirements. The proposed framework has been tested and evaluated through two different scenarios by using Contiki OS and compared with comparative algorithms. Simulation results show that GTCCF improves performance in the presence of congestion by an overall average of 30.45%, 39.77%, 26.37%, 91.37% and 13.42% in terms of throughput, end-to-end delay, energy consumption, number of lost packets and weighted fairness index respectively as compared to DCCC6 algorithm