Abstract Delay tolerant networks (DTNs) is a network evolved from mobile networks. Differing from the traditional network, which has a stable end‐to‐end transmission path, DTNs are sparse and intermittently connected mobile ad hoc network, which are widely used in harsh environments, such as battlefields, seabed, space communication networks, and so on. In DTNs, intermittent connectivity, partitioned network, long delays and node mobility characteristics make the network fail to communicate frequently, therefore, how to successfully forward the message is of extreme importance. Up to now, almost all the traditional models in DTNs use the store‐carry‐forward method. This paper proposes a novel clustered DTN routing model based on sensing node relationship strength. The routing mechanism takes advantage of the number of other nodes encountered by the nodes in the process of movement and the changes in the number of nodes to calculate the strength of the relationship between nodes, and clusters DTN routing according to the strength of the relationship between nodes. Moreover, the relationship between nodes in a cluster and other clusters is used to transmit messages between clusters, and messages are transmitted within clusters according to the strength of the relationship between nodes. Simulation results show that the routing mechanism not only increases the success rate of message transmission, but also reduces the transmission delay of messages and improves network performance.

The Internet of Things (IoT) paradigm has applications in many domains and is growing these days progressively. The applications are e-business, e-healthcare, and e-transportation. Recently, the container microservices-based Mobile Cloud Computing (MCC) has gained popularity, a lightweight framework compared to the monolithic virtual machine-based system. MCC combines fog nodes or cloud nodes with a base station to run the applications. However, storing the sensitive data of IoT applications on the untrusted nodes and failure of services are critical challenges in the existing architecture. This study proposes a novel microservices-based by combining fog and cloud services with efficient schemes. The first scheme is the Latency Aware Task Assignment Algorithm, which determines the optimal assignment of tasks to minimize the makespan of all applications. The second scheme is Fully Homomorphism Encryption, which ensures data security before an offload to any external assistance for execution. The final one is the Failure Aware, which handles any transient failure during application execution in the architecture. The experimental results show that the recommended architecture improved resource utilization, and the proposed schemes satisfied the security demand while reducing the makespan of applications.

Intrusion detection systems are widely implemented to protect computer networks from threats. To identify unknown attacks, many machine learning algorithms like neural networks have been explored for anomaly based detection. However, in real-world applications, the performance of classifiers might be fluctuant with different data sets, while one main reason is due to some redundant or ineffective features. To mitigate this issue, this study investigates some feature selection methods and introduces an ensemble of Neural Networks and Random Forest to improve the detection performance. In particular, we design an intelligent system that can choose an appropriate algorithm in an adaptive way. In the evaluation, we study the feasibility of our approach with KDD99 data set and evaluate its practical performance with a real data set collected from a Honeynet environment. The experimental results indicate that as compared with similar approaches, our approach can overall provide a better result, through identifying important and closely related features.

We designed a communication system for law enforcement officers to use when conducting explosive detection searches with multiple agencies. Dogs trained in explosive detection work alongside human handlers to form a K9 team, which are an integral part of these searches. Officers in K9 teams have a strong bond and communication with these dogs, but noisy locations, long distances, and crowded spaces present challenges. In addition, other officers assigned as backup often lack the experience to read the cues from the canine, which hinders the speed and effectiveness of the team. Coordinating a search with teams from different municipalities presents challenges due to a lack of standard collaboration tools. Getting the right information as quickly as possible saves lives, whether this information is about the areas that have been searched or the location of an explosive device. We hope that in addition to increasing public safety, our system will make working conditions safer for law enforcement officers and their canines.