Wireless Sensor Networks constitute an important part of the Internet of Things, and in a similar way to other wireless technologies, seek competitiveness concerning savings in energy consumption and information availability. These devices (sensors) are typically battery operated and distributed throughout a scenario of particular interest. However, they are prone to interference attacks which we know as jamming. The detection of anomalous behavior in the network is a subject of study where the routing protocol and the nodes increase power consumption, which is detrimental to the network’s performance. In this work, a simple jamming detection algorithm is proposed based on an exhaustive study of performance metrics related to the routing protocol and a significant impact on node energy. With this approach, the proposed algorithm detects areas of affected nodes with minimal energy expenditure. Detection is evaluated for four known cluster-based protocols: PEGASIS, TEEN, LEACH, and HPAR. The experiments analyze the protocols’ performance through the metrics chosen for a jamming detection algorithm. Finally, we conducted real experimentation with the best performing wireless protocols currently used, such as Zigbee and LoRa.

Location information for wireless sensor nodes is needed in most of the routing protocols for distributed sensor networks to determine the distance between two particular nodes in order to estimate the energy consumption. Differential evolution obtains a suboptimal solution based on three features included in the objective function: area, energy, and redundancy. The use of obstacles is considered to check how these barriers affect the behavior of the whole solution. The obstacles are considered like new restrictions aside of the typical restrictions of area boundaries and the overlap minimization. At each generation, the best element is tested to check whether the node distribution is able to create a minimum spanning tree and then to arrange the nodes using the smallest distance from the initial position to the suboptimal end position based on the Hungarian algorithm. This work presents results for different scenarios delimited by walls and testing whether it is possible to obtain a suboptimal solution with inner obstacles. Also, a case with an area delimited by a star shape is presented showing that the algorithm is able to fill the whole area, even if such area is delimited for the peaks of the star.

Resource efficiency in wireless ad hoc networks has become a widely studied NP-problem. This problem may be suboptimally solved by heuristic strategies, focusing on several features like the channel capacity, coverage area, and more. In this work, maximizing coverage area and minimizing energy consumption are suboptimally adjusted with the implementation of two of Storn/Price’s Multiobjective Differential Evolution (DE) algorithm versions. Additionally, their extended representations with the use of random-M parameter into the mutation operator were also evaluated. These versions optimize the initial random distribution of the nodes in different shaped areas, by keeping the connectivity of all the network nodes by using the Prim–Dijkstra algorithm. Moreover, the Hungarian algorithm is applied to find the minimum path distance between the initial and final node positions in order to arrange them at the end of the DE algorithm. A case base is analyzed theoretically to check how DE is able to find suboptimal solutions with certain accuracy. The results here computed show that the inclusion of random-M and completion of the algorithm, where the area is pondered with 60% and the energy is pondered with 40%, lead to energy optimization and a total coverage area higher than 90%, by considering the best results on each scenario. Thus, this work shows that the aforementioned strategies are feasible to be applied on this problem with successful results. Finally, these results are compared against two typical bioinspired multiobjective algorithms, where the DE algorithm shows the best tradeoff.

For almost one decade, the academic community has been working in the design and analysis of new lightweight primitives. This cryptography development aims to provide solutions tailored for resource-constrained devices. The U.S. National Institute of Standards and Technology (NIST) started an open process to create a Lightweight Cryptography Standardization portfolio. As a part of the process, the candidates must demonstrate their suitability for hardware implementation. Cost and performance are two of the criteria to be evaluated. In this work, we present the analysis of costs and performance in hardware implementations over five NIST LWC Round 2 candidates, COMET, ESTATE-AES/Gift, LOCUS, LOTUS, and Oribatida. Each candidate’s implementation was adapted to the Hardware API for Lightweight Cryptography for fair benchmarking of hardware cores. The results were generated for Xilinx Artix-7 xc7a12tcsg325-3. The results indicate that it is feasible to achieve the reduction of each solution below 2000 LUTs and 2000 slices where some of them (the variants of ESTATE-AES/Gift) are below 850 LUTs and 600 FF when they are included in the LWC CryptoCore.