A Survey on Multimedia-Based Cross-Layer Optimization in Visual Sensor Networks

Abstract

Visual sensor networks (VSNs) comprised of battery-operated electronic devices endowed with low-resolution cameras have expanded the applicability of a series of monitoring applications. Those types of sensors are interconnected by ad hoc error-prone wireless links, imposing stringent restrictions on available bandwidth, end-to-end delay and packet error rates. In such context, multimedia coding is required for data compression and error-resilience, also ensuring energy preservation over the path(s) toward the sink and improving the end-to-end perceptual quality of the received media. Cross-layer optimization may enhance the expected efficiency of VSNs applications, disrupting the conventional information flow of the protocol layers. When the inner characteristics of the multimedia coding techniques are exploited by cross-layer protocols and architectures, higher efficiency may be obtained in visual sensor networks. This paper surveys recent research on multimedia-based cross-layer optimization, presenting the proposed strategies and mechanisms for transmission rate adjustment, congestion control, multipath selection, energy preservation and error recovery. We note that many multimedia-based cross-layer optimization solutions have been proposed in recent years, each one bringing a wealth of contributions to visual sensor networks.

Energy and Delay Optimization of Heterogeneous Multicore Wireless Multimedia Sensor Nodes by Adaptive Genetic‐Simulated Annealing Algorithm

“…Cross-layer protocols improve the communication performance by merging the information of different layers jointly; for example, the network layer can decide how to route by incorporating the congestion information from the transport layer and the link quality value from the MAC layer. This incorporation of different layers improves the Wireless Communications and Mobile Computing 3 communication quality and maximizes the network lifetime [13][14][15]. Data prediction technique builds the prediction model which describes the data evolution of the sensed phenomenon within certain error bounds.…”

“…Data compression [2][3][4], data aggregation [5,6], topology control [7,8], energy-aware 2 Wireless Communications and Mobile Computing protocol [9][10][11][12][13][14][15], data prediction [16], and sink mobility [17,18] were commonly applied to conserve the energy, while delay-aware routing [10,19] and delay-aware MAC [20,21] were widely used to optimize the response delay. These past works improved the energy and delay performance of the WMSN significantly.…”

XL-WMSN: cross-layer quality of service protocol for wireless multimedia sensor networks

“…In this section, we focus on schemes in which MAC and network layer jointly provide real-time routing and prioritized medium access. A detailed survey on cross-layer protocols for WMSNs is given in [10].…”

A routing mechanism based on the sensing relevancies of source nodes for time-critical applications in visual sensor networks

“…Different routing approaches have been investigated in recent years for wireless sensor networks, addressing issues as energy-efficiency, delay constraints and data packet prioritization [3][8] [9]. We are mainly concerned in surveying optimizations in the way packets are routed in wireless multimedia sensor networks, where the transmission requirements are more stringent when compared with traditional scalar sensor networks.…”

“…Although such requirement may incur in additional energy consumption, the actual impact depends on the adopted MAC technology and wireless radio [19], and the inclusion of optimization information in data packets is a common approach largely adopted by the academic community [9].…”