人體感知網絡(BAN)因為移動性和完全分佈的性質，它們之間的干擾減輕一直是個關鍵的挑戰。當兩個或更多附近的BAN同時傳輸時，身體傳感器的資料會因為發生碰撞而重新傳輸消耗更多的能量，每個BAN因此花費更多的時間來傳輸，而降低了總網路容量。為了降低能耗並提高網絡容量，在本文中，我們提出了一個基於CSMA/CA的負載自適應媒介存取通訊協定稱之為LAMP。基於馬爾科夫鏈分析，LAMP可以根據操作頻道中的負載動態選擇合適的競爭視窗大小，並在當前頻道接近擁塞時自動切換到另一個頻道。然而，為了估計頻道負載，每個BAN必須等待收集所有相鄰BAN的信息，這可能需要很長時間來感知。LAMP因此採用共享相鄰BAN的信息來減少時間開銷，並且通過Bloom過濾器最小化消息開銷。因此，LAMP在跟不共享信息的方法的比較下，改進了偵測超載頻道至少10倍的時間。模擬結果顯示，LAMP在吞吐量和能量消耗方面分別比傳統CSMA/CA協議高出42.8%和50%，並同時保持高度的公平性。

Interference mitigation among body area networks (BANs) has been a critical challenge due to their mobility and fully distributed nature. When two or more nearby BANs transmit simultaneously, collisions may occur and body sensors therefore consume more energy for re-transmission. Each BAN thus spends more channel time, which lowers the total network capacity. To reduce energy consumption and better the network capacity, in this paper, we propose a CSMA/CA-based Load Adaptive MAC Protocol for inter-BAN, called LAMP.Based on Markov chain analysis, LAMP can dynamically choose a proper contention window size according to the load in the operating channel and automatically switch to another channel when the current channel is near congested. However, to estimate the channel load,each BAN has to wait to collect information of all neighboring BANs, which may take along time to estimate the channel load. LAMP therefore adopts sharing the information of neighboring BANs to reduce time overhead, and minimizes the message overhead by Bloom filter. Consequently, LAMP improves the time of detecting overloaded channel at least 10×, as a comparison of the method without sharing information. Simulation results also show that LAMP outperforms traditional CSMA/CA protocols in terms of throughput and energy consumption up to 42.8% and 50%, respectively. Meanwhile, LAMP keeps high fairness.

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