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In the next-generation heterogeneous wireless networks, designing authentication protocols that meet the demand of mobile users/applications is a challenge. This paper proposes authentication and re-authentication protocols for 4G wireless networks, in particular, LTE-Advanced (LTE-A), WLAN, and WiMAX-Advanced (WiMAX-A) interworking architecture. The proposed protocols are applicable to 5G networks. With the consideration of the existing standard authentication protocols, a new set of authentication and re-authentication protocols has been reinvented to provide fast and secure handovers (HO) in the current 4G and the next 5G networks. The proposed authentication protocols can be invoked when the users perform a vertical HO (between different networks) for the first time, whereas the re-authentication protocols can be invoked when the users perform a horizontal HO (within the same network domain). These protocols provide an efficient method to protect user identity and reduce the burden on the authentication server (AS) during the sequential handovers. The results of the analytical model show that the proposed protocols achieve better performance than standard and other protocols. The reduction of handover cost, handover delay, and energy consumption in the proposed protocols reaches up to 22%, 44%, and 17%, respectively. In addition, the verification tools show that the proposed protocols are secure, dependable, and prevent all types of authentication and secrecy attacks.

Wireless sensor networks (WSNs) are often deployed in hostile environments, where an adversary can physically capture some of the sensor nodes. The adversary collects all the nodes’ important credentials and subsequently replicate the nodes, which may expose the network to a number of other security attacks, and eventually compromise the entire network. This harmful attack where a single or more nodes illegitimately claims an identity as replicas is known as the node replication attack. The problem of node replication attack can be further aggravated due to the mobile nature in WSN. In this paper, we propose an extended version of multi-level replica detection technique built on Danger Theory (DT), which utilizes a hybrid approach (centralized and distributed) to shield the mobile wireless sensor networks (MWSNs) from clone attacks. The danger theory concept depends on a multi-level of detections; first stage (highlights the danger zone (DZ) by checking the abnormal behavior of mobile nodes), second stage (battery check and random number) and third stage (inform about replica to other networks). The DT method performance is highlighted through security parameters such as false negative, energy, detection time, communication overhead and delay in detection. The proposed approach also demonstrates that the hybrid DT method is capable and successful in detecting and mitigating any malicious activities initiated by the replica. Nowadays, crimes are vastly increasing and it is crucial to modify the systems accordingly. Indeed, it is understood that the communication needs to be secured by keen observation at each level of detection. The simulation results show that the proposed approach overcomes the weaknesses of the previous and existing centralized and distributed approaches and enhances the performance of MWSN in terms of communication and memory overhead.

Underwater wireless sensor network (UWSN) is the enabling technology for a new era of underwater monitoring and actuation applications. In this network, data aggregation and forwarding are intensely constrained due to channel impairment, and therefore require due consideration. One way to address the data collection of UWSN is by enhancing the routing protocol using the Opportunistic Routing (OR) technique. This article proposes a normalized advancement based opportunistic routing protocol called NA-TORA. NA-TORA is a geographically opportunistic routing protocol in which the next-hop forwarder is selected based on Normalized Advancement (NA). NA is calculated from Expected Transmission Count (ETX) and node' s energy consumption to find an optimal forwarding node. However, the forwarded data may not be received on the designated sink node due to the existence of a void node in the data forwarding route. To overcome the issue of void nodes, we have incorporated a void node detection and avoiding mechanism on NA-TORA, called NA-TORA with VA. The proposed scheme recursively detect void nodes and avoid these nodes to participate in data routing by utilizing the angle of transmission adjustment and transmission range extension method. The novelty of this work lies within its data transmission phase, where normalized advancement is used to select a potential candidate forwarder. Apart from that, the proposed routing protocol operates in two different modes, i.e., standard operating mode (NA-TORA), and void avoidance mode (NA-TORA with VA). Comprehensive simulations were performed to compare the performance of NA-TORA and NA-TORA with VA with some well-known existing routing protocols.

A crucial performance concern in distributed decentralized environments, like clouds, is how to guarantee that jobs complete their execution within the estimated completion times using the available resources’ bandwidth fairly and efficiently while considering the resource performance variations. Formerly, several models including reservation, migration, and replication heuristics have been implemented to solve this concern under a variety of scheduling techniques; however, they have some undetermined obstacles. This paper proposes a dynamic job scheduling model (DTSCA) that uses job characteristics to map them to resources with minimum execution time taking into account utilizing the available resources bandwidth fairly to satisfy the cloud users quality of service (QoS) requirements and utilize the providers’ resources efficiently. The scheduling algorithm makes use of job characteristics (length, expected execution time, expected bandwidth) with regards to available symmetrical and non-symmetrical resources characteristics (CPU, memory, and available bandwidth). This scheduling strategy is based on generating an expectation value for each job that is proportional to how these job’s characteristics are related to all other jobs in total. That should make their virtual machine choice closer to their expectation, thus fairer. It also builds a feedback method which deals with reallocation of failed jobs that do not meet the mapping criteria.