Although Software-Defined Networking (SDN) introduces architectural innovations, it retains fundamental network properties. As a result, Low-rate Denial of Service (LDoS) attacks, which exploit bottleneck links and TCP congestion control mechanisms, still pose a serious threat to SDN. Currently, to accurately detect LDoS attacks at lower average attack rates, many methods focus on extracting and analyzing single-dimensional features. However, these methods are often complex and offer only limited improvements in detection accuracy. Moreover, critical security vulnerabilities in mainstream mitigation strategies highlight their inability to ensure long-term stability. To this end, we propose BDTM, a cross-dimensional bidirectional detection and traceability mitigation scheme. Through attack parameter estimation with a precision of 0.1s, BDTM achieves precise detection of LDoS attacks that incorporate IP spoofing. In terms of mitigation, we have identified, verified, and resolved critical vulnerabilities in existing mainstream mitigation strategies for the first time. Upon detecting an attack, BDTM rapidly mitigates the ongoing anomaly while performing reverse-flow tracing to pinpoint the attacking host. Ultimately, BDTM enforces port-level isolation targeting the attacker rather than the attack flows, ensuring more effective and comprehensive mitigation. Experimental results demonstrate that BDTM achieves a high detection accuracy of 98.85%, with an average response time of just 5.67s when performing attack traceability.

Wireless sensor networks are widely used in various applications such as military surveillance, wildlife monitoring, industrial process monitoring and so on. Detecting and recovering from coverage holes is a major issue in wireless sensor networks. The gradual energy depletion of sensor nodes with limited battery energy leads to dysfunction of some of the nodes, thus creating holes in the monitored area. In this paper, propose a method to detect and recover coverage holes in wireless sensor networks. This method first divides the network into equally sized units, and then selects a representative node for each unit, called an agent. Then, the percentage of each cell covered by nodes can be accurately calculated and holes can be detected. Finally, the holes are recovered using the average of the key perceptual intersections as the initial value of the global optimal point of the particle swarm optimization algorithm. Simulation indicates show that the algorithm reduces the network energy consumption, reduces the distance travelled by the mobile nodes and increases the percentage of network holes recovery as compared to other algorithms.

The mobile wireless sensor networks have been used in many popular applications. However, the limited battery capacity of sensor nodes is still one of the key issues in wireless sensor networks (WSNs). The new and effective way is to use wireless energy transfer and rechargeable lithium batteries to solve this problem. To improve the charging energy effectiveness, we introduce a stop-wait scheme that allows the mobile charger (MC) to stop and wait to charge the coming mobile sensors. The lifetime of the WSNs is considered to assure that each sensor will not run out of energy. The simulation experiment shows that our algorithm improves the energy usage effectiveness.

In mobile wireless multimedia sensor networks (MWMSNs) image compression task coordination, the existing methods without considering the dynamic changes of the processing ability and location of cooperative nodes, which will cause frequent interruptions of image compression tasks and task data re-transmission. To solve these problems, an image compression task cooperation algorithm based on dynamic alliance (ATDA) is proposed. The image compression task is divided into image transfer subtask and image compression subtask based on the task stable execution time and the principles and constraints of task decomposition. Simulation results show that the proposed algorithm can realize the task load balancing of the alliance collaboration nodes and reduce the execution time of the image compression task and energy consumption.

Recent studies have shown that the flow table size of hardware SDN switch cannot match the number of concurrent flows. Combined SDN Forwarding Element (CFE), which comprises software switch and hardware switch, becomes an alternative approach for tackling this problem. Because software switch has lower lookup speed than hardware switch, different proportions of traffic allocated to software switches in CFE have different effects on the delay bounds of all flows entering CFE. As delay-guarantee is a nontrivial task for network providers, especially with the increasing number of delay-sensitive applications, a model to analyze the delay bound given a flow allocation in CFE is important. With the one-to-one correspondence between flow allocation and rules placement solution, the analytical model can be used to evaluate and compare rules placement solutions and provide a basis for designing better rules placement solution in CFE. In this paper, we propose an analytical model for CFE based on network calculus, and then validate this model through simulations in NS-3.