Monitoring Wireless Sensor Networks (WSNs) are composed of sensor nodes that report temperature, relative humidity, and other environmental parameters. The time between two successive measurements is a critical parameter to set during the WSN configuration because it can impact the WSN's lifetime, the wireless medium contention and the quality of the reported data. As trends in monitored parameters can significantly vary between scenarios and within time, identifying a sampling interval suitable for several cases is also challenging. In this work, we propose a dynamic sampling rate adaptation scheme based on reinforcement learning, able to tune sensors' sampling interval on-the-fly, according to environmental conditions and application requirements. The primary goal is to set the sampling interval to the best value possible so as to avoid oversampling and save energy, while not missing environmental changes that can be relevant for the application. In simulations, our mechanism could reduce up to 73 the total number of transmissions compared to a fixed strategy and, simultaneously, keep the average quality of information provided by the WSN. The inherent flexibility of the reinforcement learning algorithm facilitates its use in several scenarios, so as to exploit the broad scope of the Internet of Things.
WSNs are mainly composed of wireless sensor nodes that make measurements and transmit them to a Gateway ( ). There are many solutions to reduce their number of transmissions, which include clustering, data aggregation, and data prediction. @cite_8 , the authors suggest that future measurements can be predicted by sensor nodes and s . Therefore, sensor nodes only transmit a measurement if they observe that the prediction is not correct, i.e., the real measurement differs by more than a certain threshold from the predicted value. The success of this technique highly depends on the capacity of the sensor nodes to compute efficient prediction methods that will accurately predict future values. However, sensor nodes usually have very limited computing capacities and must rely on s to regularly generate and transmit new predictions. Moreover, s and sensor nodes must share the same knowledge, which requires additional control messages and reduces the benefit of decreasing measurement transmissions, especially if predictions are not sufficiently accurate.
@cite_8
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Wireless sensor networks (WSNs) are ad-hoc networks composed of tiny devices with limited computation and energy capacities. For such devices, data transmission is a very energy-consuming operation. It thus becomes essential to the lifetime of a WSN to minimize the number of bits sent by each device. One well-known approach is to aggregate sensor data (e.g., by adding) along the path from sensors to the sink. Aggregation becomes especially challenging if end-to-end privacy between sensors and the sink is required. In this paper, we propose a simple and provably secure additively homomorphic stream cipher that allows efficient aggregation of encrypted data. The new cipher only uses modular additions (with very small moduli) and is therefore very well suited for CPU-constrained devices. We show that aggregation based on this cipher can be used to efficiently compute statistical values such as mean, variance and standard deviation of sensed data, while achieving significant bandwidth gain. Wireless Sensor Networks (WSNs) consist of a large number of nodes networked via wireless links. In many WSN settings, sensor nodes are deployed in an ad hoc manner. One important issue in this context is to detect the boundary of the deployed network to ensure that the sensor nodes cover the target area. In this paper, we propose a new algorithm that can be used to discover the boundary of a randomly deployed WSN. The algorithm does not require the sensor nodes to be equipped with positioning devices and is scalable for large number of nodes. Simulation experiments are developed to evaluate the performance of the proposed algorithms for different network topologies. The simulation results show that the algorithm detects the boundary nodes of the network with high accuracy. Wireless sensor network (WSN) brings a new paradigm of real-time embedded systems with limited computation, communication, memory, and energy resources that are being used for huge range of applications where the traditional infrastructure-based network is mostly infeasible. The sensor nodes are densely deployed in a hostile environment to monitor, detect, and analyze the physical phenomenon and consume considerable amount of energy while transmitting the information. It is impractical and sometimes impossible to replace the battery and to maintain longer network life time. So, there is a limitation on the lifetime of the battery power and energy conservation is a challenging issue. Appropriate cluster head (CH) election is one such issue, which can reduce the energy consumption dramatically. Low energy adaptive clustering hierarchy (LEACH) is the most famous hierarchical routing protocol, where the CH is elected in rotation basis based on a probabilistic threshold value and only CHs are allowed to send the information to the base station (BS). But in this approach, a super-CH (SCH) is elected among the CHs who can only send the information to the mobile BS by choosing suitable fuzzy descriptors, such as remaining battery power, mobility of BS, and centrality of the clusters. Fuzzy inference engine (Mamdani’s rule) is used to elect the chance to be the SCH. The results have been derived from NS-2 simulator and show that the proposed protocol performs better than the LEACH protocol in terms of the first node dies, half node alive, better stability, and better lifetime. Wireless Sensor Networks (WSNs) are being designed to solve a gamut of interesting real-world problems. Limitations on available energy and bandwidth, message loss, high rates of node failure, and communication restrictions pose challenging requirements for these systems. Beyond these inherent limitations, both the possibility of node mobility and energy conserving protocols that power down nodes introduce additional complexity to routing protocols that depend on up to date routing or neighborhood tables. Such state-based protocols suffer excessive delay or message loss, as system dynamics require expensive upkeep of these tables. Utilizing characteristics of high node density and location awareness, we introduce IGF, a location-aware routing protocol that is robust and works without knowledge of the existence of neighboring nodes (state-free). We compare our work against established routing protocols to demonstrate the efficacy of our solution when nodes are mobile or periodically sleep to conserve energy. We show that IGF far outperforms these protocols, in some cases delivering close to 100 of the packets transmitted while alternate solutions fail to even find a path between a source and destination. Specifically, we show that our protocol demonstrates a vast improvement over prior work using metrics of delivery ratio, control overhead, and end-to-end delay.