Distributed Intelligence Technology at the Edge of IoT Networks - AETiC 2020

1. Gabatarwa

Internet of Things (IoT) ya sauya yanayin ƙirƙira ta hanyar tattarawa da adana bayanai daga abubuwa na zahiri ko na'urori. Haɓakar na'urorin IoT ya haifar da hawan gefe (edge computing), inda ake sarrafa bayanai kusa da tushensu maimakon aika su zuwa manyan uwar garken girgije. Ya zuwa 2020, ana sa ran za a haɗa wayoyi masu hankali biliyan 50 zuwa intanet, waɗanda za su samar da kimanin bayanai na zettabyte 500.

2. Baya da Ayyukan Da Alaka

2.1 IoT Architecture Evolution

Traditional IoT architectures heavily rely on cloud-centric models, with all data processing occurring in centralized data centers. However, this approach faces significant challenges, including latency issues, bandwidth constraints, and privacy concerns. The shift toward edge computing represents a fundamental transformation in how IoT systems are designed and deployed.

2.2 Edge Computing Paradigm

Edge computing brings computation and data storage closer to the point of demand, thereby improving response times and saving bandwidth. Key paradigms include fog computing, mobile edge computing (MEC), and cloudlet architectures, each offering distinct advantages for different IoT application scenarios.

3. Distributed Intelligence Framework

3.1 Architecture Components

Tsarin hankali na rarraba ya ƙunshi manyan matakai guda uku: na'urorin gefe, sabar gefe, da kayan aikin girgije. Na'urorin gefe suna aiwatar da farkon sarrafa bayanai da tacewa, sabar gefe tana sarrafa ƙididdiga masu rikitarwa, yayin da girgije ke ba da daidaitawa na duniya da ajiya na dogon lokaci.

3.2 Samfurin Rarraba Mai Hikima

The three main intelligent distribution models include: hierarchical distribution (processing occurs across multiple levels), peer-to-peer distribution (supports direct communication between devices), and hybrid approaches (combining both methods for optimal performance).

4. Aiwatar da Fasaha

4.1 Mathematical Foundations

Ingantaccen hankali da aka rarraba za a iya bayyana shi azaman matsalar ingantaccen ƙuntatawa. Bari L_total yayi wakiltin jimlar jinkiri, wanda za'a iya bayyana shi azaman:

L_total = ∑_(i=1)^n (L_proc_i + L_trans_i + L_queue_i)

A cikin haka $L_{proc_i}$ yana nufin jinkirin sarrafa na'ura ta i, $L_{trans_i}$ jinkirin watsawa, $L_{queue_i}$ kuma jinkirin jeri. Manufar ita ce rage $L_{total}$ a ƙarƙashin iyakar albarkatun $R_{max}$ da buƙatun ingancin hidima $Q_{min}$.

4.2 Algorithm Design

Distributed intelligence algorithm employs collaborative filtering approach, edge nodes share processed insights rather than raw data. The following pseudocode illustrates the core decision process:

function distributedIntelligence(node, data, neighbors):
    // 本地处理
    local_insight = processLocally(data)
    
    // 检查本地处理是否充分
    if confidence(local_insight) > threshold:
        return local_insight
    else:
        // 与邻居节点协作
        neighbor_insights = []
        for neighbor in neighbors:
            insight = requestInsight(neighbor, data)
            neighbor_insights.append(insight)
        
        // 聚合洞察
        final_decision = aggregateInsights(local_insight, neighbor_insights)
        return final_decision
end function

5. Sakamakon Gwaji

Experimental evaluation reveals substantial system performance enhancement. Compared to pure cloud architecture, the distributed intelligence approach reduces average response time by 45% and decreases bandwidth consumption by 60%. In latency-sensitive applications such as autonomous vehicle coordination, the system achieves decision-making time below 50 milliseconds, meeting real-time requirements.

6. Yanar Fage da Aikace-aikace

Hankalin Rarraba na Gefen yana kaiwa da yawa aikace-aikace a fagage da yawa. A cikin Birane Masu Wayo, yana inganta sarrafa zirga-zirga na ainihi da daidaita amsa gaggawa. Aikace-aikacen Kiwon Lafiya sun haɗa da kula da marasa lafiya na nesa da binciken annoba. Fa'idodin Yanar Gizo na Masana'antu sun haɗa da kulawa na annabta da ingantaccen sarrafa sarkar kayan masarufi.

7. Kalubale da Hanyoyin Gaba

Manyan kalubalen sun hada da rashin tsaro a cikin tsarin rarrabawa, haɗin kai tsakanin na'urori daban-daban, da ƙayyadaddun albarkatun na'urorin gefe. Binciken nan gaba ya mayar da hankali kan rarraba hankali mai daidaitawa, hanyoyin koyon tarayya, da haɗawa da hanyoyin sadarwa na 5G/6G don haɓaka haɗin kai.

8. Bincike na Asali

Binciken da aka gabatar a cikin wannan takarda yana wakiltar babban ci gaba a tsarin IoT ta hanyar magance manyan iyakoki na samfurin girman gizo. Hanyar hankali rarrabuwar yana daidaitawa da sabon salo na lissafin gefe, kamar yadda irin wannan ci gaban da aka nuna a cikin tsarin injinan koyon na'ura kamar TensorFlow Federated. Idan aka kwatanta da hanyoyin da suka dace na taro, hankali rarrabuwar yana ba da fa'idodi masu mahimmanci na rage jinkiri da ingantaccen bandeji, musamman ma don aikace-aikacen lokaci na ainihi kamar tsarin sarrafa kai da kuma sarrafa masana'antu.

Tsarin lissafin jinkirin da aka gabatar an gina shi a kan ka'idojin ka'idojin jeri, kama da hanyoyin da ake amfani da su a cikin hanyoyin rarraba abun ciki (CDN) da bayanan bayanai da aka rarraba. Duk da haka, aikace-aikacen a cikin hanyar sadarwar IoT na gefe ya gabatar da takurawa na musamman da suka shafi bambancin na'ura da iyakokin albarkatun. Algorithm ɗin da aka gabatar yana da kamanceceniya da fasahar tace haɗin gwiwa da ake amfani da ita a cikin tsarin ba da shawara, kuma an daidaita shi don yanayin da ke da iyaka.

Idan aka kwatanta da sauran firam ɗin lissafin gefe kamar AWS Greengrass ko Azure IoT Edge, hanyar hankali da aka rarraba tana jaddada haɗin gwiwar tsarin-motsi maimakon alaƙar gefen gajimare mai matsayi. Wannan bambanci yana da mahimmanci musamman ga aikace-aikacen da ke buƙatar babban samuwa da juriya ga kurakurai. Sakamakon binciken ya yi daidai da yanayin masana'antu na rahoton Gartner, wanda ke hasashen cewa zuwa 2025, kashi 75 cikin 100 na bayanan da kamfanoni ke samarwa za a ƙirƙira su kuma a sarrafa su a waje da cibiyoyin bayanai na al'ada na tarawa.

Gudummbarin hankali na tsaro yana da muhimman tasiri da ya kamata a ci gaba da bincike, saboda yankin harin yana faɗaɗawa tare da rarraba hankali. Ayyukan nan gaba za su iya haɗa fasahar blockchain don cimma yarjejeniya mai tsaro a rarraba, kamar yadda aka bincika a cikin binciken tsaron IoT. Ana buƙatar tabbatar da ƙarfin tsarin da aka gabatar ta hanyar ƙara girman turawa, musamman a cikin yanayin da ke da dubban na'urori masu haɗin kai.

9. Manazarta

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