Large propagation delay, high error rate, low band-width and limited energy in Underwater Sensor Networks (UWSNs) attract the attention of most researchers. In UWSNs, efficient utilization of energy is one of the major issue, as the replacement of energy sources in such environment is very expensive. In this paper, we have proposed a Cluster Depth Based Routing (cDBR) that is based on existing Depth Based Routing (DBR) protocol. In DBR, routing is based on the depth of the sensor nodes: the nodes having less depth are used as a forward nodes and consumes more energy as compared to the rest of nodes. As a result, nodes nearer to sink dies first because of more load. In cDBR, cluster based approach is used. In order to minimize the energy consumption, load among all the nodes are distributed equally. The energy consumption of each node is equally utilized as each node has equal probability to be selected as a Cluster Head (CH). This improves the stability period of network from DBR. In cDBR Cluster Heads (CHs) are used for forwarding packets that maximizes throughput of the network. We have compared our results with DBR and Energy Efficient DBR (EEDBR). The simulation result validates that cDBR achieves better stability period and high throughput comparatively to DBR and EEDBR.

Trust represents the belief or perception of an entity, such as a mobile device or a node, in the extent to which future actions and reactions are appropriate in a collaborative relationship. Reputation represents the network-wide belief or perception of the trustworthiness of an entity. Each entity computes and assigns a trust or reputation value, which increases and decreases with the appropriateness of actions and reactions, to another entity in order to ensure a healthy collaborative relationship. Trust and reputation management (TRM) has been investigated to improve the security of traditional networks, particularly the access networks. In 5G, the access networks are multi-hop networks formed by entities which may not be trustable, and so such networks are prone to attacks, such as Sybil and crude attacks. TRM addresses such attacks to enhance the overall network performance, including reliability, scalability, and stability. Nevertheless, the investigation of TRM in 5G, which is the next-generation wireless networks, is still at its infancy. TRM must cater for the characteristics of 5G. Firstly, ultra-densification due to the exponential growth of mobile users and data traffic. Secondly, high heterogeneity due to the different characteristics of mobile users, such as different transmission characteristics (e.g., different transmission power) and different user equipment (e.g., laptops and smartphones). Thirdly, high variability due to the dynamicity of the entities’ behaviors and operating environment. TRM must also cater for the core features of 5G (e.g., millimeter wave transmission, and device-to-device communication) and the core technologies of 5G (e.g., massive MIMO and beamforming, and network virtualization). In this paper, a review of TRM schemes in 5G and traditional networks, which can be leveraged to 5G, is presented. We also provide an insight on some of the important open issues and vulnerabilities in 5G networks that can be resolved using a TRM framework.

Adoption of the Internet of Things for the realization of smart cities in various domains has been pushed by the advancements in Information Communication and Technology. Transportation, power delivery, environmental monitoring, and medical applications are among the front runners when it comes to leveraging the benefits of IoT for improving services through modern decision support systems. Though with the enormous usage of the Internet of Medical Things, security and privacy become intrinsic issues, thus adversaries can exploit these devices or information on these devices for malicious intents. These devices generate and log large and complex raw data which are used by decision support systems to provide better care to patients. Investigation of these enormous and complicated data from a victim’s device is a daunting and time-consuming task for an investigator. Different feature-based frameworks have been proposed to resolve this problem to detect early and effectively the access logs to better assess the event. But the problem with the existing approaches is that it forces the investigator to manually comb through collected data which can contain a huge amount of irrelevant data. These data are provided normally in textual form to the investigators which are too time-consuming for the investigations even if they can utilize machine learning or natural language processing techniques. In this paper, we proposed a visualization-based approach to tackle the problem of investigating large and complex raw data sets from the Internet of Medical Things. Our contribution in this work is twofold. Firstly, we create a data set through a dynamic behavioral analysis of 400 malware samples. Secondly, the resultant and reduced data set were then visualized most feasibly. This is to investigate an incident easily. The experimental results show that an investigator can investigate large amounts of data in an easy and time-efficient manner through the effective use of visualization techniques.

Trust investigation in 5G, which is the next-generation wireless network, is still at its infancy. This research proposes a hybrid trust model for the selection of a legitimate (or trusted) forwarding (relay) entity and to countermeasure intelligent attacks against a route selection scheme. The hybrid trust model has a centralized entity (i.e., a centralized controller C c ) that provides the security level of the operating environment to network entities, and distributed entities that identify malicious or legitimate entities in the network. When the security level of the operating environment is high (low), the distributed entities learn more (lesser) from their respective operating environment. While legitimate entities can use artificial intelligence, such as reinforcement learning (RL), to enhance their trust models, the malicious entities can also use artificial intelligence to increase the detrimental effects of their attacks and minimize their likelihood of being detected. Our proposed trust model is feasible with the introduction of artificial intelligence and the central controller (C c ) in 5G to support the hybrid trust model. We have explained in detail our proposed model that reinforcement learning based hybridization of trust model can enhance the performance of the network interms of learning and tackling intelligent attacks, whereby achieving context awareness and detection of malicious entities.

Trust investigation in 5G the next-generation wireless network, is still naive. The article investigates into a trust model based on reinforcement learning (RL) to select a legitimate (or trusted) forwarding entity (or node). RL can be embedded in an entity (that can be legitimate or malicious) to enable to learn a higly dynamic and heterogenous environments. The legitimate entity (e.g., a node) uses RL to select the best possible next hop forwarder (a relay) and to successfully transmit the desired packet towards the destination while the malicious entities exist in the network. The malicious entity can also use RL to launch an attack (i.e., intelligent attack) without being detected. Simulation results show that the legitimate entity can learn fast (i.e., converge fast) at a higher learning rate (i.e., $\alpha=0.9$ ) and perform well in terms of trusted forwarder selection. Nevertheless, the malicious entity can also learn fast and launch successful attacks (i.e., affecting the throughput by dropping the packets) without being detected due to its fugitive nature.

In this paper, we propose a region based cooperative routing protocol (RPCRP). This protocol performs analysis of amplify and forward technique over Rayleigh fading channels. The source node sends the sensed signal to the destination and available relay nodes. At the destination node, bit error rate (BER) is checked on the basis of which, either positive or negative acknowledgement (ACK or NACK) is sent to the source and relay nodes. If the positive feedback is received from the destination node, the relay nodes drop the packet. However, in case of negative feedback, the best relay node amplifies the signal. After the signal is amplified, it is forwarded to the destination node. Moreover, the mobile sinks (MSs) change their position after some time and cover the whole network are also deployed. The nodes that lie within the transmission range of MSs forward their data directly to the sink. Also, the mathematical equations for the total SNR gain and outage probability are verified by simulations. Results show that RBCRP outperforms incremental best ralay technique (IBRT) in terms of throughput and network lifetime. Also, the mathematical analysis for outage probability shows that RBCRP is 62 % more better than IBRT.