CrossZig: Combating Cross-Technology Interference in Low-Power Wireless Networks

Hithnawi, Anwar; Li, Su; Shafagh, Hossein; Gross, James; Duquennoy, Simon

doi:10.1109/ipsn.2016.7460663

Cited by 42 publications

(20 citation statements)

References 38 publications

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“…Those works focus on the following 4 directions: (i) interference cancellation [5,7,8,13,24,39,41] captures (crosstechnology) interference and remove it by using high power and high sampling-rate SDR. (ii) Corruption recovery retransmits the corrupted part whenever there exists interference [15] or makes Zig-Bee packets more robust via coding [22,26]. However, corruption recovery sacrifices spectrum and energy.…”

Section: Related Workmentioning

confidence: 99%

Exploiting WiFi Guard Band for Safeguarded ZigBee

Chae

Wang

Kim

2018

Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems

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Cross-technology interference (CTI) from dense and prevalent wireless has become a primary threat to low-power IoT. This paper presents G-Bee, a CTI avoidance technique that uniquely places ZigBee packet on the guard band of ongoing WiFi traffic, which effectively safeguards the packet from WiFi interference. Such design ensures reliable ZigBee communication even under saturated WiFi traffic where traditional ZigBee is considered inoperable. Technical highlight is in lighweight WiFi guard band capture mechanism using ZigBee PHY layer samples directly accessible in various commercial ZigBee chip. Another exclusive feature of G-Bee is spectrum-synchronized low duty cycling-by utilizing guard bands of periodic WiFi beacons, active slots are effectively synchronized to spectrum availability (i.e., guard band) for significant delay improvement. Extensive evaluations on our prototype system demonstrates G-Bee PRR over 95% where legacy ZigBee drops to below 15% under significant interference with hundreds WiFi users and reduction of low duty cycle delay by 87.5%, all of which are achieved with a light computational overhead of 0.3%. CCS CONCEPTS • Computer systems organization → Sensor networks; • Networks → Wireless personal area networks;

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Section: Related Workmentioning

confidence: 99%

Exploiting WiFi Guard Band for Safeguarded ZigBee

Chae

Wang

Kim

2018

Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems

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“…When the system also cannot be scheduled, SAA then reallocates slots to accelerate the transmission speed of unscheduled flows until the flows can be scheduled (lines 14-21). Finally, SAA updates l i and returns L the set of nodes that need to be replaced (lines [22][23]. The theorem is as follows.…”

Section: Saamentioning

confidence: 99%

Transmission scheduling for mixed-critical multi-user multiple-input and multiple-output industrial cyber-physical systems

Xia

Jin

Kong

et al. 2017

International Journal of Distributed Sensor Networks

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Wireless sensor networks are widely used in industrial cyber-physical system installations, where high reliability and the need for real-time data are the two main characteristics. A large amount of real-time data can be transmitted to its destination on time using a reasonable periodic allocation of a node's transmission slots. However, a flow may miss its deadline when flow conflicts occur. When such missed deadlines occur regularly, system performance may degrade, and when the flow is critical, such data losses can result in errors or cause disasters. To address this issue, we introduce multiuser multiple-input and multiple-output technology and a mixed-critical system into an industrial cyber-physical system. When an error occurs or when demand changes, the multiuser multiple-input and multiple-output nodes can switch their transmission mode, changing to a high-criticality configuration to meet the system's new needs. Hence, we first propose a heterogeneous multiuser multiple-input and multiple-output system model. Based on this model, we propose a slot analyzing algorithm that guarantees system schedulability by reallocating slots for each node after replacing conflict nodes with multiuser multiple-input and multiple-output nodes. By considering both system schedulability and cost, the slot analyzing algorithm also reduces the number of multiuser multiple-input and multiple-output nodes required. Then, to further reduce the number of multiuser multiple-input and multiple-output nodes in an industrial cyber-physical system, we propose a priority inversion algorithm that improves schedulability by adjusting slot allocations before replacing conflict nodes with multiuser multiple-input and multiple-output nodes. By reducing the use of multiuser multiple-input and multiple-output nodes, the priority inversion algorithm achieves better performance than the slot analyzing algorithm when the system is in a high-criticality mode. Evaluation results show the effectiveness and efficacy of our approaches.

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“…As a solution, TIIM [13] extracts features from corrupted packets to quantify the interference conditions instead of identifying the interferer, thereby, the interference condition can be mapped to a specific mitigation technique. Nonetheless, TIIM does not present an implementation of these mitigation techniques; only in their follow up work, CrossZig [8], the authors have implemented an adaptive packet recovery and an adaptive FEC coding for this. All these solutions, however, are reactive, depending on the prevailing channel conditions, and do not aim to predict the white spaces through modeling, which is instead our goal in this paper.…”

Section: Related Workmentioning

confidence: 99%

Modeling WiFi Traffic for White Space Prediction in Wireless Sensor Networks

Dhanapala¹,

Marfievici²,

Palipana³

et al. 2017

2017 IEEE 42nd Conference on Local Computer Networks (LCN)

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Cross Technology Interference (CTI) is a prevalent phenomenon in the 2.4 GHz unlicensed spectrum causing packet losses and increased channel contention. In particular, WiFi interference is a severe problem for low-power wireless networks as its presence causes a significant degradation of the overall performance. In this paper, we propose a proactive approach based on WiFi interference modelling for accurately predicting transmission opportunities for low-power wireless networks. We leverage statistical analysis of real-world WiFi traces to learn aggregated traffic characteristics in terms of Inter-Arrival Time (IAT) that, once captured into a specific 2nd order Markov Modulated Poisson Process (MMPP(2)) model, enable accurate estimation of interference. We further use a hidden Markov model (HMM) for channel occupancy prediction. We evaluated the performance of i) the MMPP(2) traffic model w.r.t. real-world traces and an existing Pareto model for accurately characterizing the WiFi traffic and, ii) compared the HMM based white space prediction to random channel access. We report encouraging results for using interference modeling for white space prediction.

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CrossZig: Combating Cross-Technology Interference in Low-Power Wireless Networks

Cited by 42 publications

References 38 publications

Exploiting WiFi Guard Band for Safeguarded ZigBee

Exploiting WiFi Guard Band for Safeguarded ZigBee

Transmission scheduling for mixed-critical multi-user multiple-input and multiple-output industrial cyber-physical systems

Modeling WiFi Traffic for White Space Prediction in Wireless Sensor Networks

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