An overlay and distributed approach to node mobility in multi-access wireless networks

Abstract

Nowadays many manufacturers are building mobile devices with multiple interfaces. Thus, users have access to different types of wireless access networks, which often, as for WLAN and cellular systems, coexists independently. The challenge is to make such multiple access networks to cooperate to have ubiquitous access and enhanced user quality of service. In this paper we present a scheme to allow inter-technology mobility by the introduction of an overlay network, which works on top of current (and future) networks. The proposed architecture controls all the aspect related to the mobility management: mobile node localization, handover decision and execution. The approach is distributed: it is the mobile node that decides which network to use, based on the offered service quality and the cost of the communication of the available networks, and triggers the handover execution directly to the corresponding host, using optimized SIP-based procedures. The overlay network copes with the mobile node localization. We implemented our solution in the laboratory to prove its validity and to test performance using real equipment. We also simulated the scheme using ns-3 to extend the evaluation to large scale deployments. In both test environments, our solution demonstrates high accurateness in selecting the network with the best quality as well as in supporting seamless vertical handover.

Utilizing Active Sensor Nodes in Smart Environments for Optimal Communication Coverage

“…Thus, many scholars have proposed different solutions. For example, Dini et al [13] proposed a random motion algorithm (AORDM) for improving network connectivity. Fadlullah et al [14] proposed a dynamic trajectory control algorithm for improving the throughput and delay of a network by adjusting the coordinates of the center nodes and the radius of the coverage area.…”

“…In this case, an active node can be used as an intermediary cascade node to ensure reliable transmission. Assuming that the motion of the node is a function of time, the motion trajectory equation of the active node can be represented [13]- [15] as:…”

“…where P T is the transmit power of the node, η is the path attenuation factor, d is the distance between the active node and the target node, and X σ is the observed noise. Following [13], the parameters were set to P T = 55dBm, η = 4 (outdoors), mean X σ = 0, and variance σ = 5. In addition, the network delay was set to 3 seconds according to [13], [26], and [27], and the run time was 300 seconds.…”

“…Following [13], the parameters were set to P T = 55dBm, η = 4 (outdoors), mean X σ = 0, and variance σ = 5. In addition, the network delay was set to 3 seconds according to [13], [26], and [27], and the run time was 300 seconds. For consistency with several real applications, e.g., indoor positioning, geological detection and water quality monitoring, the speeds of the mobile nodes were set to 5 m/s, 10 m/s and 15 m/s.…”