IET Wireless Sensor Systems最新文献

Monitoring physiological signals during regular life might provide many benefits including early detection of abnormalities and tracking the severities of diseases. A wireless connection between the passive sensor and the scanner eliminates the obtrusive wires, resolves battery-related issues, and makes it easy-to-use. We have previously proposed a wireless resistive analogue passive sensor technique that operates with the help of inductive coupling. The variation of resistive physiological transducer (secondary side) leads to amplitude modulation on the scanner coil (primary side). The design of printed spiral coil (PSC) on printed circuit board, significantly affects the performance of the overall system in terms of sensitivity, the output voltage change as a reflection of the transducer change. To optimize the PSC's profile and maximize the sensitivity, we employ three methods: iterative, analytical, and genetic algorithm (GA). The GA optimized PSCs, as the best result, have been fabricated and the measurement showed a sensitivity of 0.72 mƱ which has 5% (8.8%) deviation from the simulation (theoretical) results. This method can be utilized to design a PSC pair in near-field applications to transfer amplitude modulation with various sizes and fabrication constraints.

Recently, the area coverage problem has emerged in the directional sensor network (DSN), where the sensor's sensed area depends on its working direction and viewing angle. This study has proposed a new algorithm based on the Voronoi diagram, called prioritized geometric area coverage (PGAC), to increase DSN's covered area. In a Voronoi diagram, all internal points of a convex polygon (cell) formed around a sensor are closer to the sensor than any other sensor. Therefore, the best sensor for covering a Voronoi cell area is the corresponding sensor of the cell. In contrast to similar approaches, PGAC considers the relation between the cell area and the sensor's covered area when selecting a sensor's working direction. It categorizes Voronoi cells, based on their geometric sizes, into three categories. In each category, PGAC adjusts the sensor's working direction to maximize the covered area and minimize the overlapping between adjacent cells. It also turns off redundant sensors for extending the network lifetime. Our simulation results showed that PGAC increases the covered area and decreases the number of active sensors compared to similar methods.

Non-uniform node distribution in a randomly deployed network causes inefficient routing when some abnormality or events at certain parts of the network cannot be detected due to a coverage hole problem. Existing works to improve routing efficiency in randomly deployed network utilise mobile sink to prolong nodes lifetime is either centred around the static sink or movement through random patterns. Few works had considered multiple mobile sinks to route packets in such network distribution. In fact, important aspect such as collaborations among mobile sinks is not well considered, where the decision made by each mobile sink in most existing approaches tend to maximise the individual sink's utility rather than benefiting wider parts of the network. Thus, a decision based on consensus may benefit more parties and eventually lead to better decision. A distributed collaboration mechanism may assist nodes mobility in improving routing efficiency. Thus, the authors proposed the consensus-based routing protocol, which aims to improve routing efficiency in randomly deployed network, by deciding relocation of mobile sinks based on coverage factor, in order to reduce energy consumption, providing more balance sinks distribution and better network coverage. Simulation results show good performances of proposed approach in terms of nodes lifetime, energy consumption, delay, packet delivery ratio and coverage hole, compared to other existing protocols.

Internet of Things (IoTs) as a new network pattern for the intelligent world usually uses wireless sensor networks (WSNs) as a perception layer which consisted of numerous number of sensor nodes scattered in the environment to gather intended information. The selected information then is sent to a base station (BS) to be sent to cloud server for further processing. Since the energy of sensor nodes is limited, the most significant challenge in these networks is reducing the energy consumption of the network. It is proved that dividing the network to clusters can significantly reduce the energy consumption. One of the most popular clustering protocols in WSNs is the Low-Energy Adaptive Clustering Hierarchy (LEACH). In this protocol, cluster heads (CHs) are selected randomly which results in poor performance in real scenarios. In this article, a new energy aware CH selection algorithm is proposed which selects CHs based on the residual energy, the position and centrality of nodes. It uses a variable range upon which the centrality and the number of neighbours of each node are calculated. Simulation results show that the proposed algorithm outperforms LEACH, Multi-hope Routing with LEACH (MR-LEACH) and Enhanced Multi-hop LEACH (EM-LEACH) in terms of reducing energy consumption, increasing network lifetime and improving network reliability.

The finite energy of batteries associated with wireless sensor networks is a major constraint, which limits its lifetime. One of the methods to overcome this major limitation is the energy harvesting (EH) systems. There are many energy sources available nowadays, but solar energy is flexible, mature and is an external power source; so, it is broadly utilised for EH in the wireless sensor network to enhance the life of the network. The solar EH technique along with the low energy adaptive clustering hierarchy protocol is applied. The charging and discharging curves of the battery and energy status of the nodes are propounded. The simulation results demonstrate that the lifetime of the battery and the network gets augmented after applying the solar EH technique.

In target tracking scenarios, a suitable sensor selection method in combination with designing mobile sinks (MSes) movement path is a promising solution to maximise the lifetime of the wireless sensor network (WSN). The lifetime optimization problem is non-deterministic polynomial-time hardness (NP-hard) and its optimal solution requires an exhaustive search with the exponential complexity. To tackle this issue, the original problem is separated into two more tractable sub-problems: first, the MSes' assignment and their positions' calculations, second, the SN assignment problem. The SNs assignment problem is an integer programing, which is relaxed to the more tractable form. Moreover, its solution results in a priority function which is combined with a numerical search algorithm to select the SNs. In the proposed framework, based on the estimated path of each target, a MS is assigned to the target. Besides, the MSes optimum position in the next rounds are calculated. Afterwards, for each target at each round, sensing nodes (SNs) are selected based on the targets' position and the MSes' position. Simulation results show that proposed solution increases the network's lifetime considerably in comparison with the benchmark algorithms while its complexity is much lower than that of the optimal exhaustive search algorithm.

A weighted stochastic gradient algorithm is proposed for cost-efficient tracking of unknown, correlated spatial signals from randomly distributed sensor observations in localized wireless sensor field. The algorithm is implemented in spatial modelling and spatial tracking phases. In spatial modelling phase, the algorithm finds the model parameters, and in spatial tracking phase, it updates these parameters. The spatial signal is modelled with its M iso-contour lines at equally spaced levels $�{��\left\{��\ell ��\right\}��}_{�k�=�1�}^{�M�}$ and the sensors with sensor observations in Δ margin of these contour levels report to the fusion centre (FC) for spatial monitoring purpose. Based on progressive learning and in successive iterations, the algorithm improves its findings of the signal strength's range, and the spatial, temporal and spectral attributes of the signal. To reduce the cost, in each iteration, only a subset of wireless sensors transmits the observations to the FC, in response to its query. In this article, the percentage of the reporting sensors to the FC is defined as the algorithm's cost. With importance sampling perspective, the sample space is reduced to those sensors whose observations are within a Δ margin of atleast one of these M contour levels. The Δ margin is pruned or enhanced using the proposed weighted stochastic gradient algorithm, dynamically in order to reduce the spatial tracking cost. The evaluation results show that after spatial modelling, spatial tracking is drastically of low cost and its performance is better than that of the conventional stochastic gradient method. The modelling error, the cost and the convergence of the proposed algorithm are investigated extensively, in this article. Spatial correlation in signal distribution and the coordinates of the wireless sensors are the only initial assumptions in spatial monitoring of the unknown signal distribution. The main purpose of this algorithm is low-cost identification of unknown correlated spatial signals from sensor observations, over time. An example for application of the proposed algorithm is environmental monitoring using wireless sensor observations.

The fast growth in Internet-of-Things (IoT) applications has increased the number of end-devices communicating over the Internet. As these resource-constrained devices are connected over the Internet, they are exposed to various security and privacy issues. Traditionally, to provide end-to-end security, research efforts have been carried out using elliptic curve cryptography-based authentication schemes. These existing schemes use a single set of elliptic curve parameters to generate the public and private keys, which are prone to be hacked under intense cryptanalysis. Thereby to increase the security strength the proposed method uses two sets of dynamic elliptic curves for performing digital signature and encryption processes. The method is evaluated on a client-server model using the Raspberry Pi-3 device. Herein, the choice of elliptic curves is made random at the client node and the reference of each elliptic curve is exchanged with the server during the key-exchange process. The choice of elliptic curves is delegated to the client machine to avoid the unwanted delay that would be encounter if server had to choose the curve. This is done because the client senses the data at regular intervals of time and forwards it to the server securely. Experiments were conducted to measure the time taken for hashing function, key generation, signature generation, signature verification, encryption and decryption process. On comparing the proposed DEC-LADE with cBLAKE2b, an improvement was seen at 13.76%, 2.57%, 18.36%, 6.12%, 9.91% and 6.08% less time, respectively, than LWDSA with mBLAKE2b. Theoretical and real-time security evaluations are performed for man-in-the-middle attack, replay attack, and a denial-of-service attack.

Fog computing (FC) archetypes is in focus recently for its potential to utilise resources optimally by the Internet of Things (IoT) network bearing ample end-devices. This enhances the quality of service (QoS) to the proximity of end-users as latency-free processing is obtained in cloud-IoT-based environs. Mining industry will encounter a level-up due to these technological advancements like fog-IoT-based systems. Monitoring mining activities such as blasting, slope monitoring, and miner tracking are time-sensitive. Hence, instant response is demand of extreme mine environment so that life and property are not compromised. New opportunities emerge in mining since the FC approach addresses these demands depending on the demand of the situation/user. The parameters of integrating the state-of-art of fog, IoT, and cloud architectures are provided, and a Fog-IoT mines monitoring (FIoTMM) system for real-time monitoring of open-cast mines is proposed. This work also envisions the applications and future trends of FC concerning IoT and cloud. It emphasises how key features of fog help the system in achieving lower network influx, reduced latency, resource utilisation, and immediate data computation and storage because these services take place closer to point of data generation. This data can be immediately visualised/accessed and used to generate early warning in the mines.

Communications between the Internet of Vehicles in smart cities helps increase the awareness and safety among drivers. However, the channel congestion problem is considered as a key challenge for the communication networks due to continuing collection and exchange of traffic information in dense environments. The channel congestion problem degrades the efficiency and reliability of the ad hoc network. Therefore, the adaptation of the data rate and power control is considered as one of the effective solutions to mitigate channel congestion. A new hybrid game transmission rate and power channel congestion control approach on the Internet of Vehicle networks where the nodes play as greedy opponents demanding high information rates with the maximum power level are developed. Furthermore, the existence of a Nash equilibrium, which is the optimal information rate and power transmission for every vehicle, is established. Simulation results demonstrate that the proposed approach enhances the network performance by an overall percentage of 42.27%, 43.94% and 47.66% regarding channel busy time, messages loss and data collision as compared with others. This increases the awareness and performance of the vehicular communication network.