Summary Wireless sensor networks (WSNs) have actively been considered in vast amount of applications in fields of science and engineering. The node location technology is one of the most critical technologies of WSNs. Aiming at the problem of distance vector‐hop (DV‐HOP) algorithm's excessive estimation error, we propose in this article a multi‐objective DV‐HOP localization algorithm based on differential evolution quantum particle swarm optimization (DQPSO‐DV‐HOP). First, the set of anchor nodes generated during the deployment phase that would cause large errors is eliminated, and a correction factor is introduced to modify the average hop distance to reflect the actual situation of the network better. In the node localization phase, the objective function we propose is optimized under a combination of the DE and QPSO algorithms, so the estimated results of unknown nodes are optimized and modified by using the QPSO algorithm of fast convergence, which is easy to converge to the optimal global value. Simulation results show that the localization stability, accuracy, and convergence given by the proposed DQPSO‐DV‐HOP algorithm are better than other schemes. High precision positioning algorithm can improve the accuracy of energy consumption monitoring and provide more accurate data for energy saving management.

To make up the existing deficiencies of clone attack detection methods of wireless sensor networks, a low resource consumption clone detection method (MSCD) for multi-base station wireless sensor networks is proposed. MSCD has the following characteristics: (1) Running in each ring network with base station as the center and using nodes in non-hotspot area to complete clone attack detection, which reduces the effect of clone attack detection on the network lifetime; (2) Combine the head node rotation mechanism and the backup head node mechanism to ensure the energy balance of the network; (3) The ring head node path can find clone nodes that come from different local networks, which makes the MSCD method be suitable for the whole multi-base station network. Meanwhile, in the detection domain of the head node, local broadcast is used to ensure the encounter of legitimacy verification messages and witness nodes; (4) It is proved theoretically that the clone detection probability of MSCD can reach 1 when the witness is credible. The theoretical analysis and the simulation results show that the MSCD clone detection probability is still above 98% when the number of clone nodes accounts for 10% of the total number of nodes, and the network lifetime and storage requirements are significantly better than the existing similar methods. INDEX TERMS Clone attacks, wireless sensor networks, network lifetime, multi-base station networks.

IoT era and its ubiquitous sensing raises serious security challenges such as wormhole attacks. Given these attacks may affect the location determination of the employed sensors, security can be seriously compromised. The most common and serious attack is the single wormhole one, which is the focus of this paper. One of the most employed algorithms to approach the sensor location determination is the Distance Vector Hop (DV-Hop) algorithm, which can stillbe seriously affected from wormhole attacks. To overcome the challenges of this algorithm, this article proposes a novel secure DV-Hop localization algorithm against wormhole attack (ANDV-Hop), where beacon nodes delegate their attacked neighboring nodes to broadcast data messages, and the intersection of communication range of these neighboring nodes does include wormhole nodes. For implicit wormhole attacks, close nodes to the wormhole node are selected in order to broadcast data messages, whilst the nodes within attack range remove beacon nodes at the other end of the link from the neighboring list. For explicit wormhole attack, the algorithm employs a trust model that calculates the comprehensive trust value which is obtained via a selection reward/punish coefficient, where the selected ones within the intersection zone are considered as rewarded, whilst the ones to be removed, classified as punished Experimental results show that the proposed algorithm improves detection success rate, reduces relative localization error and energy loss, showing effectiveness and reliability.